rafmacalaba/datause-encoder-data huggingface dataset:
Encouragingly, we find that the intensity and personalization of financial education support can enable individuals to undertake these more costly or difficult actions to better manage their finances. We find that individualized counseling led to significant and economically meaningful improvements in ongoing budgeting and holding a formal bank account. Furthermore, we find that the sample population receiving all three treatments in financial education, counseling and goalsetting (that is, a very high intensity treatment) exhibited larger positive changes on all outcomes we examined, relative to those who received financial education alone.

Taken together, our results confirm the hypothesis that individuals often face multiple constraints in improving financial behavior. While financial education alone may be relatively ineffective in improving outcomes, bundling such training with inexpensive but personalized add-ons, such as counseling and goal-setting, results in a potent combination, leading to meaningful impacts on both financial knowledge and behavior.

More generally, our findings suggest that traditional financial education programs, especially those implemented in a group setting with a one-size-fits-all approach, may be inadequate in equipping individuals with the appropriate tools to bridge the gap between financial knowledge and financial behavior. Nevertheless, we do not view this as evidence to warrant broad pessimism about financial education programs. Rather, this evidence builds on prior research and expands our understanding of the factors that enable financial knowledge to become financial actions – providing personally relevant support, adequate intensity of information, and overcoming behavioral limitations to change. This can enable policy makers and NGOs to design and implement more effective financial education support for development impact.

The rest of this paper proceeds as follows. Section 2 describes the sample and study design. Section 3 presents the empirical strategy, summary statistics, and randomization checks, and Section 4 discusses the results. Section 5 concludes with the implications of the study. Appendices 1 and 2 provide the content of our financial education and health videos and present the financial knowledge survey questions, respectively.

6
female wage differentials in particular, using the Oaxaca-Blinder decomposition technique.<sup>4</sup> The objective of this work has been to further understanding of the raw gender wage differential, which universally shows that men are paid more than women, and try to account for the possibility that men, on average, may be endowed to a greater extent with characteristics that are rewarded in the labor market. The remaining unexplained component is then usually interpreted as the upper bound of discrimination against women.

Has economic reform and stabilization helped women to improve their relative position in the Brazilian labor market, and so made a positive contribution to the reduction of inequality? This paper argues that this has indeed been the case, and shows that women have benefited because their endowments of human capital characteristics have improved and the degree of discrimination in the returns that women receive for those characteristics relative to men has been reduced. Most importantly women have benefited because their average position in the earnings distribution relative to men has improved.

We reach these conclusions on the basis of analysis of large scale household survey data. Brazil is unusual among developing countries in that it has a long tradition of collecting labor market data through such surveys. The Instituto Brasileiro de Geografia e Estastistica (The Brazilian Institute of Geography and Statistics) has been collecting the Pesquisa Nacional por Amostra de Domicilios, PNAD, since 1976. The data is available for each year except the census years of 1980 and 1990 and for 1994, when for budgetary 2
market rate for effective targeting through self-selection, the overall level of benefits seems to be arguably generous considering that the minimum wage of the Ready Made Garment (RMG) sector, the country’s relatively competitive sector, is at BDT 5,300 per month.<sup>9</sup> Third, EGPP requires at least 30 percent participation of women at _Union_ level for gender empowerment. Providing women with employment opportunities where female labor force participation has been traditionally low is an important development and effort toward gender equity. Lastly and most importantly, payment is entirely in cash and transferred directly to the individual’s bank account. This was expected to address program inefficiency including leakages and challenges of procuring and transporting food benefits, by ensuring accurate and timely transfers of benefits.

In Fiscal Year 2016, the annual budget of EGPP was about US$ 100 million reaching close to one million individuals nationwide. Despite the size of the EGPP and prevalence of other social assistance programs in Bangladesh, few studies are available to assess the program impacts and effectiveness. The most relevant study to this paper is Sharif and Ruthbah (2017), which investigated the determinants of EGPP participation among poor households and estimated its impact on household expenditure during the earlier stage of EGPP from 2010 to 2012. The study found that participation in EGPP was significantly associated with the household head’s political connection to local officials and influential people in villages, which highlights the need to ensure a transparent targeting and enrollment process. Building upon earlier studies, this paper focuses on evaluating the first-order impacts of EGPP on households’ consumption using more recent data.

# **3. Data and Descriptive Analysis**

## **3.1 Data and summary statistics**

Our analysis is based on locality panel survey data collected at the household and locality levels, for which the sample was drawn from the MoDMR’s nationwide beneficiary roster of the five social assistance programs described above. The first round was implemented in February and March 2015, and the second round during the same months in 2016, considering the seasonality of economic activities in rural Bangladesh. We use the main sample drawn from four traditional

> 9 Currently, Bangladesh has not endorsed its national minimum wage policy. The discussed level of national minimum wage of BDT 1,500 per month is not an official figure but can be found from www.wageindicator.org. However, due to international pressure after high profile industrial accidents in the RMG sector (including the Rana Plaza factory building collapse), the sector increased its minimum wage to BDT 5,300 per month in 2013.

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|own a bicycle<br>own a motorcycle||0.5<br>0.5||4.5<br>15.6||2.1<br>9.4|
|---|---|---|---|---|---|---|
|own a car||66.3||72.3||15.5|
|_Share of respondents who:_|**Men**|**Women**|**Men**|**Women**|**Men**|**Women**|
|have a driving license|68.9|41.7|81.9|55.3|23.5|5.7|
|_Observations_|998|997|1000|1000|894|917|

Notes: Figures are percent of respondents within gender and city. Bicycles include other two-wheeler nonmotorized vehicles. Motorcycles include other two- or three-wheeler motorized vehicles. Cars include all type of four-wheeler motorized vehicles. Driving license include any type of driving license.

# **_4.2. Correlation between travel patterns and accessibility, availability and safety of the public transport network_**

In this section, we present the correlations between the dominant modes of transportation (as presented in Table 4) used by the respondents who work, and the three measures of the quality of public transport described previously at their residential locations. The complete correlation table is presented in Annex B. that the correlation analysis confirms that, on average, the respondents located in areas with better accessibility, availability and safety of the public transport network are more likely to use public transportation and to walk, and less likely to use private buses (provided by the employer) or private vehicles While these correlations are informative, they do not account for any confounding factors (such as socio-economic characteristics of the respondents), and do not look at the labor market outcomes of the individuals. This is precisely what we will address in the next section.

# **_4.3. Role of transportation in labor market outcomes_**

In this section we present the results of the two econometric models described above. The first measures the effect of the characteristics of the public transport network (its accessibility, availability and safety) close to women’s residential locations on their likelihood of participating in the labor market, with interaction terms between transport and cities to account for potential heterogeneities of the effect of transport across cities. The second model measures the effect of the same characteristics of the public transport network close to individuals’ residential locations on their likelihood of being employed (or self-employed). In this model, we include both women and men and add interaction terms between gender, transport variables and cities. Since both models include interaction effects, in this section we present the marginal effects estimated for the three transport measures by city (and gender for the second model).

Table 7 and Table 8 present the marginal effects of each transportation measures for each city for both labor market outcomes – respectively labor force participation and employment. The results for both labor force participation and employment are presented at the same time in 28
- 6 -

<!-- Start of picture text --> Figure 1. Road density in Nepal<br>Road Density per 100 km2 Road Density per 1000 population<br>0.0 - 2.8 0.00 - 0.10<br>2.9 - 6.9 0.11 - 0.32<br>7.0 - 11.5 0.33 - 0.52<br>11.6 - 19.3 0.53 - 0.71<br>19.4 - 29.4 0.72 - 0.90<br>29.5 - 57.9 0.91 - 1.10<br>58.0 - 87.5 1.11 - 1.38<br>87.6 - 205.8 1.39 - 2.23<br>. /<br><!-- End of picture text --> Source: Central Bureau of Statistics, Nepal (2004).

Recognizing the issues in the transport sector, the government has been making efforts to improve the rural accessibility by upgrading dry-season-only roads to all-season standards and providing structures, gravel surface and Otta seal, which is a low-cost paving option (World Bank, 2005; 2007). Some of the impacts have already been observed. An interim survey of the project indicates that the people’s mobility increased by more than 20 percent and that their travel time declined dramatically from 2.6 hours to 32 minutes on average (World Bank, 2009).

However, resources available for infrastructure development continue to be limited, as in other developing countries.<sup>1</sup> Nepal spent $105 million or 1.2 percent of GDP for road development (World Bank, 2005). This is more or less equally comparable with regional countries, such as Bangladesh spending 0.7 percent of GDP on transport infrastructure and India 1.4 percent. But these may still be far below the infrastructure needs, which are estimated at 3-7 percent of GDP in typical low-income countries.

Public procurement is an important instrument to use resources wisely and efficiently (e.g., Iimi, 2006; Estache and Iimi, 2011). ―[T]he best way to find fiscal space for public investment is to eliminate waste and improve technical efficiency in public expenditure (World Bank, 2005).‖ However, how to design an efficient procurement system is still a challenging task, especially in the infrastructure sector. Infrastructure projects are often technically complex, highly customized and politically sensitive. As a result, competition for public contracts tends to be limited, and the procurement markets would likely be vulnerable

> 1 See more discussion on transport infrastructure financing in Annex I.
20 groups of transition countries identified in previous related research: “energy-efficient” economies (Central and Eastern European countries) which are assumed to have an electricityGDP elasticity of 0.9 with a growing economy; “energy-neutral economies” (the Baltic countries), assumed to have unitary elasticity; and “energy-inefficient” economies (the rest of the former Soviet republics), assumed to have an elasticity of 1.15 when GDP is growing. We use data on electricity output and official GDP for 17 ECA countries in order to obtain annual estimates of the size of the informal economy for the period 1990-2003. As baseline data, we use the same values as Johnson et al. (1997) for the initial (i.e. 1989) shares of the informal sector in those countries.

In our sample, average gross wages tended to be higher in SHI countries than in non-SHI countries between 1990 and 2004; Armenia and Tajikistan exhibited the lowest averages in 2004 (less than US$150 PPP) while Croatia and Lithuania presented the highest averages in the same year (more than US$2,500 PPP). Average total unemployment rates seem to have been very similar for SHI and non-SHI countries during 1990-2004, with Hungary and Slovenia presenting rates around 6% in 2004, and Macedonia and Poland reaching at least 19% in the same year. Registered unemployment rates and employment-to-population ratios for individuals aged 15-59 indicate a somewhat worse employment situation for SHI adopters in such period. Selfemployment and agricultural employment were lower on average for SHI countries; and both groups of countries presented very similar estimates for the average size of the informal economy: the Czech Republic and Slovakia exhibited averages lower than 16% in 2003, in contrast to averages higher than 41% for Azerbaijan and Georgia in that year. For two selected labor variables, unemployment rate and size of the informal economy, there seems to be a close, positive relationship between the rates of SHI adoption over time and their cross-country yearly
**Table 4. Welfare implications of Doha Liberalization, $bn**

||Total L|ibn|Doha|Formula|Cuts|Doha|with flex|ibility|
|---|---|---|---|---|---|---|---|---|
||||Wtd|||Wtd|||
||Wtd Ave|σ =2|Ave|σ =2|σ =5|Ave|σ =2|σ=5|
|Australia/N Zealand|16.1|16.8|4.4|4.8|6.0|1.9|2.4|3.6|
|EFTA|20|31.6|6.1|7.6|9.5|3.0|4.2|6.2|
|EU 27|135.3|180.4|45.4|58.7|81.2|29.6|39.3|52.9|
|United States|47.9|53.8|11.2|14.5|18.5|6.4|9.9|14.1|
|Canada|7.3|8.6|1.2|1.5|2.3|0.2|0.8|1.7|
|Japan|52|64.9|25.6|29.2|32.4|18.4|21.8|26.1|
|Korea and Taiwan,|||||||||
|<br>China|77.1|98.7|19.6|21.2|23.0|9.3|9.8|10.5|
|Hong Kong SAR, China|||||||||
|<br>& Singapore|28.7|29.2|3.1|3.1|2.8|2.5|2.5|2.5|
|Chile|2.2|2.1|0.5|0.4|0.3|0.2|0.2|0.2|
|Bangladesh|-0.5|0.2|-0.8|-0.2|0.0|-0.4|-0.2|-0.3|
|Brazil|21.7|30.8|7.4|9.8|15.8|4.2|4.7|6.0|
|China|-21.4|-8.6|6.3|9.7|20.9|5.7|8.9|13.9|
|Egypt|1.2|10|0.3|0.5|0.8|0.2|0.4|0.6|
|India|18.9|24.3|5.4|6.1|7.2|2.5|2.4|2.4|
|Nigeria|3|6.8|2.0|2.9|4.1|-0.1|-0.1|-0.1|
|Pakistan|4.1|4.6|0.7|0.9|1.3|0.1|0.1|0.1|
|Indonesia|2.8|3.9|1.3|1.5|3.8|1.0|1.0|1.0|
|Thailand|6.6|8.7|3.7|4.5|5.7|1.8|2.6|4.2|
|Mexico|5.7|10.1|4.1|4.7|5.5|3.7|4.7|5.8|
|SACU|3.8|14.1|1.2|1.4|1.9|0.7|1.3|2.2|
|Turkey|8.2|11.3|1.5|1.6|1.7|0.4|0.5|0.6|
|Rest of Asia|6.8|24.5|2.4|2.9|7.3|-1.6|-1.2|-0.3|
|Rest of LAC|11.8|18.5|4.9|6.4|7.6|2.2|2.5|2.8|
|Rest of the World|26.4|64.3|2.2|3.8|4.9|1.4|1.9|2.5|
|Morocco and Tunisia|3.5|6.1|1.8|2.4|3.2|0.9|1.6|2.7|
|Rest of Sub Saharan Afr|6.4|9.4|1.7|2.3|3.1|-0.6|-0.6|-0.6|
|_High income countries_|_384.4_|_484_|_116.6_|_140.6_|_175.6_|_71.3_|_90.7_|_117.6_|
|_WTO developing ctries_|_217.2_|_369.1_|_69.2_|_85.8_|_120.9_|_34.0_|_42.9_|_56.7_|
|_Low & middle inc. ctries_|_111.4_|_241.2_|_46.5_|_61.5_|_95.1_|_22.2_|_30.7_|_43.7_|
|_L. America & Caribbean_|_41.5_|_61.6_|_16.9_|_21.3_|_29.2_|_10.4_|_12.1_|_14.8_|
|_Sub Saharan Africa_|_13.2_|_30.4_|_4.8_|_6.6_|_9.0_|_0.1_|_0.6_|_1.5_|
|_World total_|_495.8 _|_725.2 _|_163.1_|_202.1_|_270.7_|_93.5_|_121.4_|_161.3_|

_Source: LINKAGE model simulations relative to the 2025 world economy_ The estimated welfare gains from a Doha scenario obtained when using the traditional trade-weighted average tariffs are somewhat difficult to compare with other studies because of the nature of the experiments conducted, improvements in the baseline 18
step of our analysis involves a series of robustness checks. We find that our results are upheld. Although we cannot rule out _all_ possible alternative rationales, or the possibility that socio-economic status may be insufficiently captured by our controls, these results lend support to the conjecture that religion might play a direct role in explaining the differences in gender differences in mortality between the Hindus and Muslims of India.

This paper proceeds as follows: Section 2 discusses the role of religion in Indian demography and establishes our conjecture; Section 3 conducts a comparative analysis of non-Dalit Hindus, Dalit Hindus and Muslims. Section 4 concludes.

# **2 The Role of Religion in Indian Demography**

The idea that religion can shape demographic behavior is not new, particularly in the context of South Asia. Most of the existing literature on religion and demography however, focuses on fertility alone. Two main theories have dominated the discussion of religious differences in Indian fertility rates. The ‘characteristics hypothesis’ proposes that the relative poverty and lower education levels of Muslims drive fertility and mortality patterns that are different than those observed among Hindus (Iyer, 2002). In contrast to this, the ‘particularized theology hypothesis’ - or the ’pure religion effect’ argues that the intellectual content of religion affects fertility directly. Proponents of this hypothesis point out that religious injunctions in favor of multiple wives, large numbers of children, and a ban on the use of contraception and abortion may encourage higher fertility among Muslims. Such arguments however, have also been made for Hindus. The Mysore Population Study conducted in 1961, for example, concluded that Hindu religious traditions in Indian society favored having many offspring (United Nations, 1961).

We believe that the focus on fertility, to the exclusion of other demographic variables such as mortality and the sex-composition of off-spring, is incomplete. For the remainder of this section, we document the differences in beliefs and norms between Hindus and Muslims that may impact their perceived benefits of sons versus daughters.

In Islam, the institutional requirements of the religion are specified in the Sharia or Islamic law which is derived from two main sources – the Koran and the Sunnah, and also the writings of the medieval theologian Al Ghazzali, often cited by Muslim clerics, who 3
Table 2: Current labor market outcomes by past migration status

||B|angladesh|||Nepal|||Pakistan||
|---|---|---|---|---|---|---|---|---|---|
||Return<br>migrants|Non-<br>migrants|p-value|Return<br>migrants|Non-<br>migrants|p-value|Return<br>migrants|Non-<br>migrants|p-value|
|Employment rate|0.74|0.88|0.00|0.50|0.64|0.00|0.79|0.91|0.00|
|Unemployment rate|0.10|0.02|0.00|0.21|0.08|0.00|0.11|0.02|0.00|
|Labor participant rate|0.82|0.89|0.00|0.63|0.69|0.00|0.89|0.93|0.00|
|Weekly working hours|57.8|54.6|0.00|48.2|49.0|0.36|46.5|51.1|0.00|
|Monthly wage (LCU)|14,192|11,052|0.00|18,081|17,123|0.47|20,653|20,492|0.82|
|_Employment type_||||||||||
|Waged worker|0.31|0.68|0.00|0.66|0.67|0.74|0.45|0.63|0.00|
|Self-employed|0.69|0.32|0.00|0.34|0.33|0.74|0.55|0.37|0.00|
|Self-employed in agr.|0.15|0.15|0.91|0.10|0.07|0.07|0.22|0.12|0.00|
|Self-employed in non-agr.|0.53|0.17|0.00|0.24|0.26|0.41|0.33|0.25|0.00|
|Self-employed w/o employees|0.65|0.31|0.00|0.25|0.21|0.07|0.31|0.25|0.00|
|Self-employed w/ employees|0.04|0.01|0.00|0.09|0.12|0.04|0.24|0.12|0.00|
|_Industry_||||||||||
|Agriculture|0.31|0.35|0.07|0.19|0.14|0.01|0.32|0.28|0.01|
|Manufacture|0.21|0.18|0.17|0.12|0.17|0.01|0.13|0.16|0.00|
|Construction|0.06|0.07|0.10|0.28|0.21|0.00|0.15|0.10|0.00|
|Retail, hotel, restaurant|0.12|0.13|0.37|0.15|0.16|0.60|0.19|0.19|0.75|
|Others|0.29|0.26|0.14|0.25|0.33|0.00|0.21|0.27|0.00|

_Source:_ Bangladesh RMS for employment type of returnees and the HIES 2016-17 for other statistics; Nepal LFS 2017-18; Pakistan LFS 2014-15 and 2017-18.

_Note:_ Sample is restricted to males aged 20-59. Sample weights are applied to summary statistics.
The second binary variable corresponds to the death of a previous household member (i.e., an adult who was living with the elderly individual at baseline in 1991) who was _while living away_ from the elderly person at the time of his/her death. We include this group in consideration of the fact that elderly individuals in this region may receive cash or in-kind support from previous household members who later reside in another household. The variable equals one if all the following conditions are satisfied: 1) the adult who died was of prime age (15-50) at the time of death; 2) the adult was part of the elderly individual’s baseline household; and 3) the elderly individual and the primeage adult were _not_ living together at the time of death. We refer to this variable in our results as “Non-co-resident PHHM who died ages 15-50.” The third binary variable corresponds to the death of non-co-resident biological children of the elderly at baseline, who again may be important through their monetary support to other household members. We construct a binary variable that equals one if 1) the elderly individual had a non-co-resident child living at baseline who would have been 15-50 in 2004, and 2) at least one such child died between 1991 and 2004. Note that this definition is slightly different from the previous ones, since here, children living elsewhere who died before the age of 15 may still be included if they would have been 15 by 2004.

Due to data restrictions, we omit an important category of prime-age adults in these definitions. An elderly individual may have been living with prime-age adults who died who were not part of the baseline sample; since the retrospective mortality questionnaire in the 2004 survey does not ask about these deaths, they will be omitted from the prime-age death variable defined above. It is possible that we are missing some important events among those who are identified as having experienced none of the three death shocks defined above.

The survey data also record the year in which prime-age individuals died for the first two adult death variables described above. In some analyses, we construct and use binary variables corresponding to the death of a prime-age household member during three periods of the panel: 1991 to 1995, 1996 to 1999, and 2000 to 2004.

9