rafmacalaba/datause-encoder-data huggingface dataset:
exhibit a wide range, from 35.91% in Denmark to 80.75% in Bangladesh. Mexico shows the largest gap, with only 20% of respondents personally endorsing the norm that “a woman’s primary role is to care for her home and children,” compared to a perceived community agreement of 66%. On the other hand, Nigeria presents an interesting contrast, where 78% of respondents personally agree with the norm, higher than the 68% they perceive as the community norm, reflecting a more conservative personal belief relative to the perceived community stance.

# **4.1.2 Relationship between Beliefs and Perceived Community Norms**

In Appendix B in Figures B1 to B3 we provide further data visualizations of the correlation between beliefs and norms at the country level. We split the analysis by gender and analyze the three norms: the broad norm of gender equality in opportunities, the male breadwinner norm, and the female caregiver norm separately.

For the broad norm regarding gender equality in opportunities, there is a significant difference by gender. Male respondents exhibit a very strong positive correlation, with a Pearson’s correlation coefficient of 0.92, indicating that their personal beliefs are highly consistent with what they perceive their community thinks. Female respondents, on the other hand, show a much lower correlation coefficient of 0.41, suggesting a significant difference between their personal beliefs in support of gender equality and their perception of societal expectations. For the breadwinner norm, there is a strong relationship between individual beliefs and perceived community norms for both genders. Male respondents show a particularly robust correlation, with a coefficient of 0.92, indicating that their own beliefs about financial responsibility in marriage are almost identical to what they view as societal standards. Female respondents also demonstrate a positive correlation, with a coefficient of 0.74. Lastly, when examining the caregiver norm, both female and male respondents show strong positive correlations between their individual beliefs and perceived community norms. Females display a correlation coefficient of 0.73, while males a coefficient of 0.92. This similarity in correlation strength, especially among males, reflects a broad acknowledgment of traditional caregiver roles as the norm within communities.

Our findings indicate that for the broad norm of gender equality in opportunities, there is a noticeable gender disparity in how individual beliefs correlate with perceived community norms, with women showing greater support for gender equality than men. For the breadwinner norm, there is a strong correlation for both genders. Across all norms, men consistently demonstrate a stronger correlation, suggesting their own personal beliefs are more closely aligned with societal expectations. The caregiver norm analysis reinforces this pattern.

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months in Ireland or Slovakia.<sup>20</sup> Severance pay is determined as two weeks of salary per year of service, with no upper limit, which is very generous. For example, a Croatian worker with 20 years of service would be entitled to 10 monthly salaries in severance pay, while his/her Slovak, Irish and Hungarian colleagues would get 2, 2.2 and 5 monthly salaries, respectively. Imagine now that a Croatian workers is fired for, say, poor performance. As proving poor performance is difficult, courts - which traditionally have exhibited a pro labor bias - tend to order job readmission and compensation amounting to forgone earnings (with no upper limit). Given lengthy court procedures, expected hiring costs are high, effectively discouraging employers from laying worker off.

_Temporary (fixed-term) employment_ is way of circumventing high costs of terminating regular employment contracts. However, the law in Croatia restricts its use by requiring that temporary contracts are signed only on exceptional basis when there is a valid and important reason.<sup>21</sup> Furthermore, the total cumulative duration of temporary employment cannot exceed three years. While many countries impose some restrictions on the use of temporary employment contracts, in Croatia they are more strict than in other countries. For example, in Hungary or Estonia the total duration of temporary contracts can be five years. Accordingly, the incidence of temporary contracts - 11 percent -- is relatively low in Croatia. For comparison, in Spain, where temporary contracts were introduced specifically to improve flexibility and increase hiring, they account for about one-third of all contracts (Dolado et al., 2002). However, despite the restrictions, temporary contracts seem to be increasingly used by Croatian employers. After all, there is a perception that they are being abused, because reportedly the majority of new hires are offered fixed-term contracts. Using the recent Labor Force Survey (2001) data we estimate the incidence of temporary employment contracts among new hires at 55 percent. The proportion of newly concluded fixed-term contracts is high, indicting weak enforcement of the relevant provision of the Labor Law. The growing popularity of fixed term contracts among employers reflects high dismissal costs in the case of permanent employment contracts, and is a source of necessary employment flexibility for employers. As

> **20** As documented earlier in the paper, the proportion of workers with long job tenure, for whom dismissal costs are substantially higher, is very high in Croatia compared with other countries. **21** Valid reasons include seasonal work, replacement of a temporary absent employee, special business needs, etc.

-44-
- Felbermayr, Gabriel J., Jasmin Gröschl, and Thomas Steinwachs (2018), “The Trade Effects of Border Controls: Evidence from the European Schengen Agreement.” _Journal of Common Market Studies_ 56213, 335–351. url: http://www.cesifo-group.de/DocDL/wp-2016-213felbermayr-groeschl-steinwachs-trade-effects-of-border-controls.pdf.

- Felbermayr, Gabriel J., Feodora A. Teti, and Erdal Yalcin (2019), “Rules of Origin and the Profitability of Trade Deflection.” _Journal of International Economics_ 121, 103248.

- Gaulier, Guillaume and Soledad Zignago (2010), “BACI: International Trade Database at the Product-Level. The 1994-2007 Version.” _CEPII, Working Paper. No 2010-23_ 2010-23. url: http://www.cepii.fr/CEPII/fr/publications/wp/abstract.asp?NoDoc=2726.

- Head, Keith and Thierry Mayer (2014), “Gravity Equations: Workhorse,Toolkit, and Cookbook.” _Handbook of International Economics_ . Ed. by Gita Gopinath, Elhanan Helpman, and Kenneth Rogoff. Vol. 4. Elsevier B.V. Chap. 3, 131–195.

- Heid, Benedikt, Mario Larch, and Yoto V. Yotov (2021), “Estimating the Effects of Non-discriminatory Trade Policies within Structural Gravity Models.” _Canadian Journal of Economics_ 541, 376–409.

- Hofmann, Claudia, Alberto Osnago, and Michele Ruta (2019), “The Content of Preferential Trade Agreements.” _World Trade Review_ 183, 365–398.

- Kohl, Tristan, Steven Brakman, and Harry Garretsen (2016), “Do Trade Agreements Stimulate International Trade Differently? Evidence from 296 Trade Agreements.” _World Economy_ 391, 97–131.

- Lamy, Pascal (2020), “Foreword.” _Handbook of Deep Trade Agreements_ . Ed. by Aaditya Mattoo, Nadia Rocha, and Michele Ruta. 1st ed. Chap. Foreword.

- Larch, Mario, Joschka Wanner, Yoto V. Yotov, and Thomas Zylkin (2019), “Currency Unions and Trade: A PPML Re-assessment with High-dimensional Fixed Effects.” _Oxford Bulletin of Economics and Statistics_ 813, 487–510.

- Mattoo, Aaditya, Alen Mulabdic, and Michele Ruta (2022), “Trade Creation and Trade Diversion in Deep Agreements.” _Canadian Journal of Economics_ 55August, 1598–1637.

- Mattoo, Aaditya, Nadia Rocha, and Michele Ruta (2020), _The Evolution of Deep Trade Agreements_ .

- Rodrik, Dani (2018), “What Do Trade Agreements Really Do?” _Journal of Economic Perspectives_ 322, 73–90. url: https://doi.org/10.1257/jep.32.2.73.

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primary education due to chronic disruptions, reducing years of schooling. Meanwhile, deprivations in the current school attendance indicator reflect the individual’s present circumstances. Both accumulated and contemporaneous disadvantages shape the lived realities of forcibly displaced populations.

# _5.3.1 Results of intrahousehold inequalities among the MPI poor, education_

As observed in Alkire, Ul Haq, and Alim (2019), girls’ education affects various lifecourse outcomes, from consequences for human dignity to human capital for economic development to child survival (Currie and Moretti 2003, Mare and Maralani 2006, Send 2009, Hong et al. 2019). Table 12 evaluates intrahousehold inequalities for education by gender and displacement status,<sup>9</sup> while Figures A.1 and A.2 in the Annex present the population shares for the eligible populations.

_Table 12: Intrahousehold inequality in the education dimension_

|Country|Popula<br>Childre|tion<br>Sha<br>n (_n_)|re<br>of<br>|School-age|School-a<br>MPI po<br>school, a|ge childre<br>or househol<br>nd another|n who re<br>d where t<br>does not|side in an<br>hey attend<br>(%)|Chi-<br>squared test<br>f d|Chi-<br>squared<br>test<br>for|
|---|---|---|---|---|---|---|---|---|---|---|
||||||||||or gener||
||_IDP_||_Non-I_|_P_|_IDP_||_Non-ID_|_P_||IDP status|
||_Boys_|_Girls_|_Boys_|_Girls_|_Boys_|_Girls_|_Boys_|_Girls_|||
|ETH|3,880|3,311|401|418|54.60|41.76|||***|N/A|
|NGA|888|649|1,781|2,003|28.00|[13.56]|27.63|30.81||***|
|SOM|1,296|1,229|2,481|2,157|39.15|24.33|18.60|17.92|**|***|
|SSD|70|75|413|387|||[44.75]||N/A|[***]|
|SUD|825|767|1,512|1,533|31.78|32.98|25.80|9.45|||
|Ct|Popula<br>age Ho|tion Share<br>usehold M|of Prima<br>embers (1|ry School-<br>3+) (_n_)|Member<br>of schoo|s have com<br>ling, and an|pleted at l<br>other has|east 6 years<br>not (%)|||
|ounry|||||||||||
||_IDP_||_Non-I_|_P_|_IDP_||_Non-ID_|_P_|||
||_Males_|_Females_|_Males_|_Females_|_Males_|_Females_|_Males_|_Females_|||
|ETH|5,819|6,409|918|1,035|58.23|40.71|||***|N/A|
|NGA|1,287|1,231|3,484|3,692|[18.30]||63.45||[***]||
|SOM|2,698|2,761|5,162|5,182|[7.31]||48.47|44.09|***|[***]|
|SSD|198|230|1,368|1,595|||42.78|[34.31]|***||
|SUD|1,869|1,837|3,933|4,030|35.41|28.18|18.83|17.58|***|*|

Source: Authors’ calculations using the Ethiopia SPS (2017), Nigeria IDP Survey (2018), Somalia HFS (2017), South Sudan HFS (2017), and Sudan IDP Profiling Survey (2018). Significance at 1% ***, 5% **, and 10% * levels.

23
important to note that, after applying the sample restrictions discussed in section 2, we do not eliminate any outliers from the sample, neither in wage levels nor in changes. We let our model of measurement error deal with this problem.

The set of covariates used to estimate the notional wage change distribution varies in the two countries, reflecting differences in data availability. The Brazilian data are more comprehensive and allow us to control for gender, age, tenure and their quadratic terms, educational attainment, occupation and sector of operation, establishment size, type of labor contract, and location of the firm (in Belo Horizonte or outside the state capital). In the case of Uruguay, we control for gender, age, tenure and their quadratic terms, sector of operation, white or blue collar status, establishment size, and the location of the firm (in Montevideo vs. the rest of the country).

Table 2 reports maximum likelihood estimates of the notional wage change equation parameters and their associated z-statistics for Uruguay and Brazil in 1998-1999.<sup>7</sup> The estimated coefficients are highly significant in almost all cases. Some commonalities within the two countries emerge. As expected, individual wage growth declines nonmonotonically with age, is higher in the capital, and tends to increase with establishment size, although wages in medium/large Brazilian establishments (those with the 50 to1,000 employees) display higher wage growth than the largest firms in the sample (those with 1,000 employees or more). Perhaps surprisingly, the effect of education and type of contract on individual wage growth in Brazil is not clear, as suggested by inconsistent signs across years, and often coefficients not significantly different from zero.

Figure 4 presents the wage changes predicted by the model, as well as the observed wage changes in 1997-1998. Panel A displays the corresponding distributions for Uruguay and panel B for Brazil. The predicted histogram is obviously smoother, but the overall fit of the model to the shape of the observed wage growth histogram is quite good. If anything, the model tends to underestimate the concentration of observations around the focal points of rigidity. In the case of Uruguay, we slightly underestimate the spike at zero, and overestimate the share of wage growth observations immediately

> 7 Results for the other years are qualitatively similar to those of 1998-1999, unless otherwise stated in the text.

15
development, post disaster reconstruction, and housing guarantees made up almost 75 percent of the stimulus package.

The fiscal balances reflect only transactions on budget; they omit public investments financed by the domestic banking system off budget, a large component in China. The increase in expenditure on budget was heavily concentrated at the subnational level (table 5). Among subnational expenditures, the steepest increase in 2009 was for transport infrastructure, followed by “agriculture, water, and forestry.” The two categories accounted for 36 percent of the increase in subnational expenditures. Health and education spending accounted for 22 percent.

**Table 6 Real Annual Changes in Capital Spending in Indian States(percent)**

||_Percent of_<br>_total_<br>_spending by_<br>_all states in_|||||
|---|---|---|---|---|---|
|_State_|_2010_|_2003–07_|_2009_<sup>_a_</sup>|_2010_|_2008–10_|
|Andhra Pradesh|7.8|24.3|5.7|–15.2|–5.3|
|Bihar|7.1|17.9|9.6|23.1|16.1|
|Gujarat|5.4|17.5|–25.6|14.2|–7.8|
|Haryana|2.9|25.2|21.1|–32.9|–9.8|
|Karnataka|8.2|21.7|2.7|5.2|3.9|
|Kerala|2.3|14.4|2.0|23.4|12.2|
|Madhya Pradesh|8.8|15.5|27.9|3.9|15.3|
|Maharashtra|13.6|15.0|–13.7|–3.2|–8.6|
|Odisha (Orissa)|2.4|12.3|–15.6|18.2|–0.2|
|Punjab|1.0|30.9|–70.8|255.9|2.0|
|Rajasthan|2.7|14.5|–15.9|9.1|–4.2|
|Tamil Nadu|10.7|27.3|–42.2|111.8|10.7|
|Uttar Pradesh|11.4|27.3|7.0|–14.0|–4.1|
|West Bengal|4.6|8.3|–27.8|3.4|–13.6|
|All states|100.0|19.4|0.0|5.0|2.5|

_Sources_ : Reserve Bank of India state finances; individual state accounts; Central Statistical Organization.

a/ 2009 refers to fiscal year beginning April 1, 2009.

In India, pre-crisis fiscal stimulation measures—including increases in the food and fertilizer subsidy, a central pay hike, a farm debt waiver, and a national rural employment guarantee 21
# <u>4.1.3. Control variables</u>

# <u>4.1.3.1.</u> _<u>Water variability</u>_

While water variability is an inherent characteristic across river basins, climate change is predicted to intensify such variability. All else being equal, higher water variability has been shown to lead to political tensions between states sharing river basins (Yoffe et al. 2004; Stahl 2005: 277).<sup>4</sup> Results presented in Wolf, Stahl and Macomber (2003: 1), for example, demonstrate that “extreme events of conflict were more frequent in marginal climates with highly variable hydrologic conditions, while the riparians of rivers with less extreme natural conditions have been more moderate in their conflict/cooperation relationship.” For the most part, empirical studies investigating international conflict and cooperation over water and have utilized static measures of water availability. Given that this study aims to look at the effects of water variability on treaty resilience over time, we use a measure of past hydrological variability. In particular, we use the coefficient of variation (CV) for precipitation in each basin and sub-basin. Precipitation data from the Climate Research Unit (Mitchell and Jones, 2005) downloaded at the CGIAR website<sup>5</sup> are the input values for the precipitation CV. The CV is calculated to measure inter-annual precipitation over all monthly observations for the time period (1901-2001). Our rational for calculating the CV based on this extended time period is based on our empirical assumption that water variability is embedded in the basin’s history and that it is long-term water variability that affects conflict and cooperation over time in a given basin. The variability measure proposed here is, therefore, not year specific. We likewise follow precedent set by previous studies using a CV as well as other measures of water availability (Dinar et al. 2010; Dinar et al. 2011; De Stefano et al. 2012).

All things being equal, we expect basins that exhibit higher variability to exhibit less cooperative behavior, though some research in the hydropolitics literature related to water scarcity and cooperation (Dinar 2009; Tir and Ackerman 2009; Dinar et al. 2011) and variability and cooperation (Dinar et al. 2010) suggest that higher levels of variability may actually

> 4 According to the National Intelligence Assessment (2008) “climate change could threaten domestic stability in some states, potentially contributing to intra- or, less likely, inter-state conflict, particularly over access to increasingly scarce water resources.

> 5 http://cru.csi.cgiar.org/

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# **I. Introduction**

Women’s empowerment has been widely promoted as a key development goal. It is not only desirable in itself, but also has been linked to faster economic growth (World Bank 2011). When women have greater bargaining power to make household decisions, children’s education and health outcomes are better and the well-being of families improves (Duflo 2012). Giving women more power also changes communities’ choices in important ways, often resulting in higher overall efficiency of the society (Udry 1996; Goldstein and Udry 2005). Recognizing the importance of women’s empowerment, the United Nation included “Achieve gender equality and empower all women and girls” in its 17 Sustainable Development Goals. The World Bank too has made gender mainstreaming a priority in development assistance, identifying women’s empowerment as a key pathway to sustained poverty reduction and shared prosperity.

Empirical studies show that electrification disproportionally benefits women and girls. In households with electricity, women spend less time on household chores and are more likely to participate in income-generating activities, and girls have higher educational attainment (Samad and Zhang, 2016, 2017, 2018; Kohlin et al. 2012; IEA 2008). What is less understood is whether improvements in welfare outcomes also empower women and change gender norms and practices inside households.

This paper examines whether electricity access enhances women’s empowerment at the household level, defined as their ability to make strategic life choices, control resources, and craft decisions that affect important life outcomes (World Bank 2002). To the best of our knowledge, it is the first paper that provides empirical evidence on the causal link between electricity access and a wide range of empowerment indicators. The analysis is based on data from the India Human Development Survey (IHDS) carried out in 2011/2012. This survey provides a broad range of information on women’s involvement in household decision making. The survey covers more than 40,000 Indian households, including more than 200,000 individuals.

2
# **Table 4: Productivity and the Business Environment in Sri Lanka: Evidence from Cross-sectional Industrial Census Data**

||**(1)**|**(2)**|**(3)**|**(4)**|**(5)**|**(6)**|
|---|---|---|---|---|---|---|
|**Dependent var(in logs):**|**Output per**<br>**worker**|**Value Added**<br>**per worker**|**Output per**<br>**worker**|**Value Added**<br>**per worker**|<br>**Output per**<br>**worker**|<br>**Value Added**<br>**per worker**|
|Competition with foreign firms|0.810+++|0.754++|1.284+++|1.339+++|0.688+|0.842+++|
||(0.231)|(0.217)|(0.053)|(0.051)|(0.308)|(0.232)|
|Government Ownership|-0.581++|-0.596+|-1.132++|-1.203+++|-0.947+|-0.912+|
||(0.234)|(0.285)|(0.324)|(0.307)|(0.491)|(0.428)|
|Losses from crime, theft & disorder (% sales)|-0.104+++|-0.093++|-0.069+++|-0.053++|-0.095+|-0.073+++|
||(0.021)|(0.030)|(0.007)|(0.016)|(0.047)|(0.020)|
|Frequency of worker training|1.026+++|1.018+++|1.074+++|1.105+++|0.928+++|0.929+++|
||(0.277)|(0.254)|(0.301)|(0.253)|(0.226)|(0.194)|
|Export participation|0.430+++|0.385+++|0.264++|0.194|0.005|0.130|
||(0.066)|(0.070)|(0.109)|(0.132)|(0.124)|(0.129)|
|Internet use|-0.116|-0.163|-0.100|-0.132|-0.037|-0.143|
||(0.157)|(0.174)|(0.125)|(0.110)|(0.212)|(0.118)|
|Share of investment financed externally|0.011+++|0.011+++|0.012+++|0.012+++|0.011+++|0.011+++|
||(0.002)|(0.002)|(0.002)|(0.002)|(0.002)|(0.002)|
|Interest rate of loans|-0.014+|-0.045+++|-0.025+++|-0.052+++|0.019|-0.020|
||(0.007)|(0.012)|(0.006)|(0.008)|(0.021)|(0.021)|
|Percent of electricity from generator|-0.020+|-0.022++|-0.010|-0.010|0.014|0.001|
||(0.009)|(0.009)|(0.007)|(0.007)|(0.008)|(0.005)|
|Constant|9.449+++|8.991+++|9.731+++|9.189+++|8.395+++|8.204+++|
||(0.242)|(0.274)|(0.205)|(0.223)|(0.383)|(0.309)|
|N|322|322|322|322|322|322|
|R-sq|0.517|0.541|0.492|0.508|0.367|0.458|
|adj. R-sq|0.481|0.508|0.466|0.482|0.336|0.431|
|Sector fixed effects|Y|Y|Y|Y|N|N|
|Province fixed effects|Y|Y|N|N|Y|Y|

Notes: Standard errors in parentheses. + p<.1 ++ p<.05 +++ p<.01. Source: Sri Lanka Census of Industry, 2004. WBES.

37
percentage points more likely to distinguish bank processing fees, and 20 percentage points more likely to understand unproductive loans relative to the control group. Appendix Table 3 shows similar positive impacts on short-term financial attitudes – when hypothetically asked to give financial advice, treated individuals were 10 percentage points more likely to suggest insurance cover for a dangerous work environment and 20 percentage points more likely to suggest making a budget to track household income and expenditure relative to the control group. Table 4 and Appendix Table 4 shows these results hold in the long run as well.

Notably though, the pay for performance treatment did not lead to any significant marginal improvements over the standard curriculum on either of the aggregate measures of awareness or attitudes. The results in Table 3 and 4 hence suggest that participant motivation was not a critical barrier in improving financial knowledge in our sample.

# _4.2. Financial Behavior_

Our analysis on financial behavior comes from the endline survey. Data from this survey also helps distinguish impacts of additional treatments of individualized counseling and goal setting over traditional financial education.

# <u>Budgeting</u>

We first consider changes in household budgeting, the theme of one of the five financial education video sessions, in Table 5. In columns (1), (3), and (5) we report impacts of being invited to any financial education treatment on beliefs that budgeting is helpful (column 1), attempts to make a budget in the last six months (column 3), and making a regular monthly budget (column 5). Without distinguishing between treatments, we see a strong positive treatment effect on all these dimensions, though the effects weaken as we move from beliefs to actions and outcomes. Specifically, while those individuals invited to any financial education treatment were 22 percentage points more likely than the control group to understand the benefits of making a budget and 28 percentage points more likely to have attempted to make a budget, they are only 3 percentage points more likely to actually make a regular budget every month.

18