rafmacalaba/datause-encoder-data huggingface dataset:
# **Appendix Table 3: Dietary metrics for adults – definitions and data source characteristics**

|**Dietary intake**<br>|**Indicator definition**|**Data source**|**Year**|**Data**<br><sup>**1**</sup>|**Notes**|
|---|---|---|---|---|---|
|**variables**||||**availability**||
|**Women of reproductive**<br>|**age**<br>|||||
|**Minimum Diet**<br>**Diversity for women**<br>**(MDD-W)**|Percentage of women (15-49 y) who<br>consumed at least 5 of 10 defined food<br>groups the previous day or night.|World Gallup Poll<br>(Global Diet Quality<br>Project)|2021-2022|N=55<br>countries|The food groups include: 1.<br>Grains, white roots and tubers<br>and plantains; 2. Pulses<br>(beans, peas and lentils); 3.<br>Nuts and seeds; 4. Milk and<br>milk products; 5. Meat,<br>poultry and fish; 6. Eggs; 7.<br>Dark green leafy vegetables;<br>8. Other vitamin A-rich fruits<br>and vegetables; 9. Other<br>vegetables; 10. Other fruits.|
|**Adults (age ≥ 15 years):**<br>|<br>||||<br>|
|**Food group diversity**<br>**score**|The number of food groups consumed<br>the previous day or night, of the ten food<br>groups used in the MDD-W indicator|World Gallup Poll<br>(Global Diet Quality<br>Project)|2021-2022|N=55<br>countries|The score ranges from 0 to 10<br>expressed as an average score<br>for the population with a<br>higher score indicating<br>inclusion of more food groups<br>in the diet.|
|**Global Dietary**<br>**Recommendations**<br>**(GDR) score**|Score based on food consumption from<br>nine health-protective food groups<br>(NCD-Protect) and eight food groups to<br>limit or avoid (NCD-Risk) during the<br>previous day or night, which are<br>associated with meeting WHO global<br>dietary recommendations.|World Gallup Poll<br>(Global Diet Quality<br>Project)|2021-2022|N=55countri<br>es|The score ranges from 0 to 18<br>expressed as an average score.|
|**NCD-Protect score**|Score based on consumption during the<br>previous day or night of nine food groups<br>that are associated with meeting WHO<br>recommendations on fruits, vegetables,<br>whole grains, pulses, nuts and seeds, and<br>fiber.|World Gallup Poll<br>(Global Diet Quality<br>Project)|2021-2022|N=55<br>countries|An indicator of dietary factors<br>protective against NCDs<br>The score ranges from zero to<br>nine expressed as an average<br>score.|
|**NCD-Risk score**|Score based on consumption during the<br>previous day or night of eight food<br>groups that are negatively associated with<br>meeting WHOrecommendations on free|World Gallup Poll<br>(Global Diet Quality<br>Project)|2021-2022|N=55<br>countries|An indicator of dietary risk<br>factors for NCDs|

41
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Does improved access also translate into improved quality of services? Our results indicate it does However, self-reported quality suffers from information asymmetries and biases. For example, while examining the state of public health services in rural Rajasthan, Banerjee, Duflo, and Deaton (2004, Page 330) noted that villagers’ health is poor, the quality of public service is abysmal, and private players are unregulated and for the most part unqualified to provide the bulk of health care — and yet villagers self-reported satisfaction with the state of both their health and the health care facilities. Thus, they concluded that (p. 331) “self-reported health and well-being measures may be uncorrelated with the quality of the public facilities.” Our findings indicate that in states with low access; quality is reported to be higher. The may result from information asymmetries or biases at the respondent level.

# **6. Conclusion**

Existing definitions and measures of governance or state capacity might be too broad to determine a state’s capacity to deliver goods and services. This paper reports from a survey designed to measure service delivery in India. The new measure is motivated by the belief that good state capacity to deliver goods and services should translate into more access or coverage of all citizens, equal access to all citizens, and higher quality delivery of goods and services. Furthermore, a measure of state capacity to deliver goods should be able to explain variation in outcomes between countries, within a country (or between states), and within individual states.

We find that access in India is generally low: only 40 percent are able to apply for the goods and services they need. Furthermore, there exists significant variation between states. In states such as Kerala, for example, it is estimated that more than half the population have access to the government provided goods and services they need, but in Bihar less than 20 percent of the 43
Policy Research Working Paper 8043

# **Abstract**

It has long been recognized that household decision-making may not result in outcomes consistent with the unitary household model. Within the collective bargaining framework, consumption decisions would be driven by the spouse with greater bargaining power, while the outcomes would still be Pareto efficient. Within the non-cooperative framework, households would not achieve Pareto efficient outcomes, and under the simplest representation, bargaining power would not affect consumption decisions. This paper develops a model that allows consumption patterns and labor supply to be affected by both bargaining power and non-cooperation. The model highlights the potential gains from improving bargaining power versus increasing cooperation between spouses, and presents conditions under which relatively large gains would be expected from moving to more equitable bargaining power versus achieving intra-household cooperation. The model’s predictions are in turn tested using a unique panel data set on married couples in rural Malawi. The analysis shows that, relative to increasing wives’ bargaining power, improving cooperation between spouses would exert larger and statistically significant positive impacts on total household income and consumption expenditures per capita, as well as the share of household consumption devoted to public goods. Supported also by cross-country qualitative research, the results suggest that household public goods are relatively important to both women and men in rural Malawi, husbands’ capacity to control wives’ incomes is relatively limited, and development programs that promote intra-household cooperation could lead to greater gains in income and household public goods provision compared with interventions focusing exclusively on women’s empowerment.

This paper is a product of the Development Data Group. It is part of a larger effort by the World Bank to provide open access to its research and make a contribution to development policy discussions around the world. Policy Research Working Papers are also posted on the Web at http://econ.worldbank.org. The authors may be contacted at tkilic@worldbank.org.

_The Policy Research Working Paper Series disseminates the findings of work in progress to encourage the exchange of ideas about development issues. An objective of the series is to get the findings out quickly, even if the presentations are less than fully polished. The papers carry the names of the authors and should be cited accordingly. The findings, interpretations, and conclusions expressed in this paper are entirely those of the authors. They do not necessarily represent the views of the International Bank for Reconstruction and Development/World Bank and its affiliated organizations, or those of the Executive Directors of the World Bank or the governments they represent._ Produced by the Research Support Team
the 1997 experimental evaluation of the _Oportunidades_ program, a similar social transfer program to PAL, reveal that 0.7 percent of control group households migrated to the US within one year (Angelucci, 2015). Each household in our sample has on average one child between the ages of 0 and 6 and if we apply this migration rate in each of the 10 years between 2004 and 2013, we would expect as a rough approximation to not observe ENLACE tests for around 6.8 percent of children in our initial sample because they migrated abroad.

Third, a child could have never enrolled in school or could have dropped out of school before reaching the end of third grade. We can quantify this potential source of attrition using the PAL follow-up survey in 2005: 7 percent of the children aged 8 to 12 (the typical age of children in grades 3 through 6) report not attending school (and there is no difference across treatment groups).<sup>14</sup> Fourth, a student may have not been present on the day of the exam, or the exam was not offered at the school. We do not have data on absence rates in schools, but there is evidence that entire schools did not take the exam in certain years in areas where there is a strong representation of the National Educational Workers Syndicate ( _Sindicato Nacional de Trabajadores de la Educación_ ), a trade union representing 1.4 million Mexican teachers. In fact, the union scheduled strikes and disruptions specifically on the days of the ENLACE test as a form of protest and state-level variation in ENLACE take-up rates demonstrates their influence. For example, in Yucatan and Veracruz, where union membership is relatively low, take-up rates were constantly above 94 percent in all years between 2007 and 2013. In Chiapas, Guerrero and Oaxaca, however, where union membership is high, take-up varied considerably by year, with certain years having take-up rates of 60, 70, or 80 percent. Importantly, as these protests and disruptions are orthogonal to the original treatment assignment, including state and year fixed effects in our specifications will allow to account for potential biases induced by differences in the test take-up.

A final reason why we would not see a child’s ENLACE test is that the merging algorithm was not accurate. Details about the merging algorithm and the possible reasons for attrition are provided in the Data Appendix. Only for 2.8 percent of the individuals in our sample of interest we could not complete the merge due issues related to the algorithm, and the share does not differ across treatment and control groups.

> 14Although students are in school they might not reach the third grade during our period of observation either because they start late or because they repeat the early grades more than once. According to the estimates provided by INEE (2013), 95 percent of the children who enrolled for the first time in primary education in 2010 complied with the statutory starting age, and repetition rates in early grade are remarkably low. Furthermore, late entry and repetition rates do not differ across experimental groups.

10
# **Tables and Figures**

Table 1: Stock of Refugees, Population and Share of Refugees in Total Population for the Top 10 Receiving Countries in Europe, 2019

|**#**|**Country of**<br>**Asylum**|**Number of**<br>**Refugees**|**Country**<br>**Population**|**Rank**<br>**Population**|**Share of**<br>**Refugees (%)**|
|---|---|---|---|---|---|
|1|Sweden|253 787|10 285 453|8|_2.47_|
|2|Austria|135 951|8 877 067|9|_1.53_|
|3|Germany|1 146 682|83 132 799|1|_1.38_|
|4|Switzerland|110 162|8 574 832|10|_1.28_|
|5|Greece|80 454|10 716 322|7|_0.75_|
|6|France|407 915|67 059 887|2|_0.61_|
|7|Netherlands|94 417|17 332 850|5|_0.54_|
|8|Belgium|61 662|11 484 055|6|_0.54_|
|9|Italy|207 602|60 297 396|4|_0.34_|
|10|United Kingdom|133 083|66 834 405|3|_0.20_|

_Note: World Bank data on population and refugees: 1960 – 2019. Data refer to the total stock of refugees on the territory of the country of asylum in 2019. Share refers to the share of total number of refugees in total population. These countries are top ten countries in Europe in both the total population and the total number of refugees. Share’s calculations done by the Author._ 27
reporting changes in response to transitory deviations from trend. This concern is also mitigated by the fact that reporting in the DHS is not observable by husbands, due to measures taken by the DHS surveyors to ensure privacy and confidentiality, described above.<sup>17</sup> Anderberg et al. (2016) use data from a crime survey which, in principle, has the same issues as our data. Other studies have used more institutionalized data which have different limitations. For instance, Aizer (2010) uses hospitalization data, but only severe cases of IPV are hospitalized. Mani et al. (2012) use police reports, but many women are reluctant to report IPV to the police for fear that their husbands will either exercise backlash or be arrested and taken away (e.g. Iyenger 2009; Amaral et al. 2019).

The estimation sample includes 31 and 26 countries, respectively, for which physical and sexual violence data from the DHS are available for 2005-2016 (see Table S1.1).<sup>18</sup> Estimates are reported for each of physical and sexual violence and their sum. The study does not use emotional abuse as it is a more subjective measure of abuse. There is substantial variation in the incidence of physical and sexual violence across countries; see Figures 1(a) and 1(b).<sup>19</sup>

# Unemployment rate shocks

Data on male and female unemployment rates are obtained from the World Development Indicators (WDI), sourced from the ILOSTAT database.<sup>20</sup> Although violence measures are only available for

> 17 Only 0.94% of women in the sample were selected for the domestic module but not interviewed due to lack of guaranteed privacy.

> 18 The sample size for sexual violence is smaller, because in five countries only one of the questions on sexual abuse is included, so that the measure of sexual violence in these countries is not comparable to the rest of the sample. As a result, these countries are excluded from the sexual violence sample (see Table S1.1).

> 19 There is significant coincidence of physical and sexual violence: 19.81 percent of women reported experiencing some form of violence over the last twelve months; of these women, 24.23 percent report both physical and sexual violence. Of the sample of women who reported suffering some form of violence in the last twelve months, 65.81 (9.96) percent reported physical (sexual) violence only.

> 20 The ILO estimates are based on either household labour force surveys or population census data, with the restriction that they must be representative of the whole country, with no geographic limitation. More information on the construction of the estimates is given in Bourmpoula et al. (2015). These rates are positively correlated to within-survey employment rates, with a correlation coefficient of 0.299 with female unemployment rates in the DHS survey. Men’s employment is less correlated (0.030) but this is not surprising because the quality of data on male unemployment in the DHS is low: it is collected in only 18 of our 31 countries, and where it is collected, it is missing in around 67% of cases.

8
**The most prevalent form of PPP in distribution is by divestiture, where greenfield projects dominate in transmission.** The types of PPPs also evolved over time. Up to the first half of the 1990s, PPPs in distribution took place only through the sale of stakes to strategic investors, whereas since the second half of the 1990s concessions also accounted for a sizeable proportion of PPPs (see Fig. 4).

**Table 1**

# **Explanatory Variables Influencing Private Investment in T&D** **<mark>(</mark> Ex** **<mark>p</mark> ected Relationshi** **<mark>p)</mark>**

|**Variables**|**Definition**|**Exp. Sign **|
|---|---|---|
||**SECTORAL GOVERNANCE VARIABLES**||
|**Introduction of a regulatory**<br>**agency**<br>|= 1 since the year of establishment of a regulatory agency<br>= 0 otherwise|**+**|
|_REG_|||
|**Sequencing of policy**<br>**(introduction of private**<br>**sector first in distribution)**|= 1 when PPPs have been introduced first in distribution and then in<br>generation<br>= 0 hi|**+**|
|_SEQU_|oterwse||
||**ECONOMYWIDEGOVERNANCE VARIABLES**||
|**Control of Corruption**<br>_wgi_cc_|= measures the perceptions of the extent to which public power is<br>exercised for private gain, including both petty and grand forms<br>of corruption, as well as "capture" of the state by elites and<br>private interests. (Kaufmann and Kray World Governance<br>Indicator)|**+**|
|**Degree of democracy**<br>**_Polity2_**|= Revised Combined Polity Score: This variable is a modified version<br>of the POLITY variable to facilitate the use of the POLITY regime<br>measure in time-series analyses. It ranges from +10 (strongly<br>democratic) to -10 (strongly autocratic), based on 6 indicators.|**+**|
||<br>**SHORT RUN FINANCIALCRISES**||
|**East Asian and Russian**<br>**Crisis**<br>_d_FC1_|= 1 for the 5 years after 1997<br>= 0 otherwise|-|
|**Latin American Crisis**<br>_dFC2_|= 1 for the 5 years after 2001<br>= 0 otherwise|-|
|___|<br>**LONG RUN MACROECONOMIC CONTROLS**||
|**GDP per capita**<br>_ln_GDP_|= GDP per capita, PPP (constant 2005 international $ in logarithmic<br>terms)|**+**|
|**Population**<br>_lnPo_|= total population|**+**|
|__p_<br>|**ENVIRONMENTALSUSTAINABILITY FACTORS**||
|**PPP Investment in**<br>|= committed investment in renewables (US$ million)|**+**|
|**Renewables**<br>**Reion**|<br>**REGIONAL CONTROL**||
|**g**<br>_d_region1, 2, 3,4,5,6_|Dummies , 1=EAP, 2=ECA, 3=LAC, 4=MNA, 5=SAR, 6=SSA||

7
6 discusses the implications of our results. We summarize and conclude the paper in Section 7.

# 2. **Previous Research**

# **2.1  Salinity in Bangladesh**

In Bangladesh, work on salinity has advanced rapidly in recent years. Sarwar (2005) and SRDI (1998a,b, 2000, 2010) have documented changes in salinity that have accompanied coastal subsidence and thermal expansion of the ocean. Detailed assessments of salinization have employed two principal methods. One approach focuses on simulation of salinity changes in rivers and estuaries, using hydraulic engineering models whose results are compared with actual measures (Bhuiyan and Dutta 2011; Aerts et al. 2000; Nobi and Das Gupta 1997). Another approach focuses on salinity changes in local soil and groundwater, using surveys and descriptive statistics (Mahmood et al. 2010; Khan et al. 2008, 2011; Haque 2006; Sarwar 2005; Rahman and Ahsan 2001; Hassan and Shah 2006; Karim et al. 1982, 1990). In the most comprehensive study to date, Dasgupta et al. (2014a) use new monitoring data to develop detailed projections of river salinity through 2050 in Bangladesh’s coastal region. Dasgupta et al. (2014b, 2014c) extend the projections to groundwater salinity and study its impacts on agriculture and infant health.

# **2.2  Salinity and Road Maintenance**

In this paper, we consider the impact of salinization on road infrastructure. The engineering literature has devoted considerable attention to road maintenance problems created by salinity (Gokhale and Pundhir 1985; Obika et al.1989; McRobert and Foley 1999; Wilson 1999; O’Flaherty 2003). In brief, penetration of a road surface by saline water leads to progressive blistering, cracking and pulverization, as dissolved salt reacts with road 4
-7-

Looking at the timing of default over the life of a credit program illustrates some of the Issues behind default rates. The history of programs not requiring any collateral--making all farmers eligible--shows that the recovery rate Is<sup>unusually high at</sup> the beginning, but declines gradually over a program's life. The reasons<sup>for this demise</sup> seem to be declining screening quality over time, lax supervislon,<sup>and stronger Incentlves</sup> to default as the prospect of future loans diminishes. For example,<sup>the BIMAS credit</sup> program In Indonesia, implemented In 1970, reports recovery rates<sup>of 95 percent for the</sup> first two years. After five years, however, recovery rates dropped<sup>to 60 percent.</sup> To some extent, the reasons for those high levels of default<sup>can be attributed</sup> to a predominant self-serving confusion that exists on the farmer's part<sup>regarding the</sup> nature of credit. It Is not unusual that farmers perceive the loans as grants or welfare. In fact, as reported by Llpton 11980], In some South Asian languages<sup>the word</sup> used for loans from the government (tagal, taccarl) means "assistance,<sup>grant".</sup> Therefore, the reluctance to repay those loans should not be surprising. Growing evidence Indicates that the risks of default on loans are greater for<sup>large farmers who</sup> are nevertheless charged lower Interest rates than small farmers<sup>(see Ames E1974],</sup> Dadhich E1971]). Thus, those who benefit most from tolerated default are the<sup>big</sup> farmers whose default/loan ratlo Is highest as reported by Lipton<sup>(1981].</sup> This distinction, of course exacerbates the regressive nature of subsidized<sup>credit policies.</sup> Furthermore, the policies of loan and crop guarantees ought<sup>to be seen as</sup> partially responsible for those low recovery rates or tolerated default.<sup>Many countries</sup> (e.g., Mexico, Costa Rlca, Philippines,-Sri Lanka, India) provide some<sup>of that form of</sup> Insurance. The objective Is to Induce lenders to provide more loans to<sup>a target group</sup> by shifting part of the recovery risk to other agencles. Such policies<sup>have the effect</sup>