rafmacalaba/datause-encoder-data huggingface dataset:
<!-- Start of picture text --> Figure 9: Components of Unadjusted Gross Private Savings<br><!-- End of picture text --> <!-- Start of picture text --> 20 -<br>a. 15<br>2<br>5<br>1951 1958 1965 1972 1979 1986 1993<br>-Household Saving - - - - - Corporate Saving Total<br>Source: National Accounts.<br><!-- End of picture text --> <!-- Start of picture text --> Figure 10: Components of Household's Unadjusted Disposable<br>60 -____ Income<br>50<br>a. 40..........<br>20<br>% 20<br>-210<br>1951 1958 1965 1972 1979 1986 1993<br>tGOS . Salar-eSa.ar es Direct Taxes x-- TR+TR<br>Source: National Accounts. Depreciation of physical assets is added to the GOS during 1951-1969.<br>Figure 11: Components of Gross Private Corporate Savings<br>20 _____<br>a. 10<br>z<br>-0<br>-10 I I I I I I I I I I I I I I I I I ' III ' ' '<br>1951 1958 1965 1972 1979 1986 1993<br>- _ --- - GOS - Distributed Profits Direct Taxes X TR+TRj<br><!-- End of picture text --> Source: National Accounts. Depreciation of physical assets is added to the GOS during 1951-1969.

Source: National Accounts. Depreciation of physical assets is added to the GOS during 1951-1969.

14
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**Table 1: Outline of the UYEP process (selection and allocation of participants)**

|**Step**|**Activity**|**Participants**|
|---|---|---|
|1|Publicity campaign of the program|National Capital District Commission|
|2|Application|Interested youth|
|3|Participants selected|The screening survey firm<br>(implementation agency), under the<br>oversight of the Project Management<br>Unit (PMU) and with input from<br>community leaders, determines<br>applicants’ eligibility based on the ESS.|
|4|Basic Life Skills Training (BLST) and<br>Literacy and Numeracy test + EBS|All program participants|
|5.1|Component 1: YJC|Majority of participants. This<br>component is targeted to vulnerable<br>youth with limited education, and who<br>need short‐term employment to earn<br>some money.|
|5.2a|Component 2a: PET training for one<br>month|Participants that are rated above the<br>70th percentile on completion of the<br>basic life skills training and the basic<br>literacy and numeracy test|
|5.2b|Component 2b: OJT for 5 months|Participants that have at least 80%<br>attendance during the PET training then<br>progress to the OJT for 5 months.|

Source: Compiled by authors. Note: The project design has subsequently evolved and may change in the future In the past, selected YJC graduates, such as those who achieved perfect attendance during the 40 days of public works, were also graduated to the SDES. This incentive proved to be very effective in ensuring high attendance rates at the YJC. It was eventually phased out in 2015, however, as too many YJC graduates maintained perfect attendance. Now, a small proportion of the YJC who have demonstrated strong commitment and worked well under the YJC are provided extra days of semi‐skilled work under the YJC scheme itself. This typically consists of building concrete footpaths and drainage within the city, and has so far proved to be popular with the youth.

There is now a wider range of interventions offered to youth joining UYEP, especially for those joining the YJC stream. All the new possible pathways through the UYEP project are shown in Table 2 below. The inputs provided by UYEP now range from the minimum interventions of BLST + 40 days of work on public works, to about 6 months of various inputs (the most intensive set being BLST + YJC + PET + OJT). The additions to the YJC stream have been introduced to incentivize those on YJC to keep working, and offer them a chance to graduate to further training, and work options. The project will need to continue to evaluate which of the pathways is suitable for different groups of youth (e.g. for youth with more education versus those who are illiterate) and what combination of interventions has the most impact.

8
# **2.2.3 Mechanism: Interaction with population density**

If open defecation is, indeed, stunting children’s growth by causing chronic enteric infection, then height outcomes should be consistent with this mechanism. In particular, children who are more likely to be exposed to other people’s fecal pathogens due to higher population density should suffer from larger effects of open defecation. For example, Ali et al. (2002) show that higher population density is associated with greater cholera risk in a rural area of Bangladesh, and Spears (2012a) finds a greater effect of India’s Total Sanitation Campaign in districts with higher population density.

To test this conjecture, I construct a crude measure of “open defecators per square kilometer”: the product of population density per square kilometer times the fraction of people reporting open defecation. Figure 4 reveals that this measure of exposure to fecal pathogens (in logs, due to wide variation in population density) visibly predicts average child height. The regression in panel (a) of the figure explains 65 percent of variation in child height. Notably, India occupies the bottom-right corner of the graph, with high rates of open defecation and very high population density. Does population density add predictive power beyond open defecation alone? The final column of table 1 adds an interaction between open defecation and population density. The interaction term is statistically significant, and the interaction and population density are jointly significant with, an _F_ 2 _,_ 63 statistic of 4.5 ( _p_ = 0 _._ 0149) in column (a) and _F_ 2 _,_ 57 statistic of 4.5 ( _p_ = 0 _._ 0155) in column (b).

A further implication of this mechanism is that open defecation will have a steeper association with child height in urban places than in rural places (Bateman and Smith, 1991; Bateman et al., 1993). Table 4 investigates this using two additional collapsed datasets, one containing only the urban observations in each DHS survey and one containing only the rural observations. Although GDP per capita is not available for urban and rural parts of countries, urban and rural women’s height controls can similarly be computed from the 12
5

# Tabie 2

# Growth rates of output per capita, 1965 to 1989

||GDP pe<br>Annuai|r capita<br>averages|
|---|---|---|
|Country group|<br>1965-89|1989-89|
|Low- and middia-incoma economies|1.4%|0.1%|
|Low-income economies<br>|0.5%<br>|0.2%<br>|
|Middie-income economies|2.1%|0.3%|
|Sub-Saharan Africa|0.6%|0.5%|
|Highest - Botswana|9.4%|7.8%|
|Lowest - Niger|3.2%|4.8%|
|East Asia|4.1%|3.6%|
|Highest-Korea|7.7%|8.2%|
|<br>Lewest - Philippines|1.1%|-1.8%|
|South Asia|1.7%|2.3%|
|Highest - Pakistan|2.3%|3.0%|
|Lowest - Bangladesh|0.1%|0.8%|
|Latin America and the Caribbean<br>|1.0%<br>|-1,2%<br>|
|Highest-60-89Brazil,80-89Colombia|4.2%|1.3%|
|<br>Lowest - Nicaragua|-1.8%|-3.9%|
|OECD|2.6%|2.0%|
|Highest-Japan|4.5%|3.4%|
|<br>Lowest - 65-89 New Zealand, 80-89 Netherlands|1.2%|1.4%|
|Socialist economies (low range, high range)|Low|High|
|China(1965-88)||5.4%|
|<br>Algeria (1965-88)||2.7%|
|Yugosiavia (1965-88)||3.4%|
|Czechoslovakia (1948-88)|0.8%|3.5%|
|Hungary(1948-88)|1.7%|5.1%|
|<br>Poland(1948-88)|0.1%|3.6%|
|<br>Bulgaria(1948-80,1948-88)|2.8%|5.5%|
|<br>Soviet Union (1860-85)|1.6%|4.5%|
|Averageoflast§socialisteconomies|1.4%|4.4%|

All averages are unweighted. Oil-dominated countries have been exciuded.

## Sources:

Non-socialist economies taken from WOR 1990 and upa.ued 1989 with World Bank data Socialist economies as follows: Algena - from Summers and Heston (1988) China - from Statistical Yearbook of China 1984.

Yugoslavia - from Statistical Yearbook of the SFRY, various years _ . Czechosiovakia, Hungary, Poland, and Bulgaria - from Fischer and Gelb (1990), UN (1948), and WOR 1990. ; Soviet Union - High rate is from official numbers, and low rate is from Selyunin & Khanin (1987).
Figure 2: Count of children and childcare hours in the last 7 days, by provider of care and school closure status <!-- Start of picture text --> 80 A. Providers of household childcareJanuary-March 2021 80 B. Change in respondent childcarehours with school closures<br>70 All household adults 70<br>60 60<br>During school closures, women<br>50 50<br>Total hours per child<br>Before closures, women<br>40 40<br>Respondent During school closures, men<br>30 30 After full reopening, women Before closures, men<br>After full reopening, men<br>20 All household children 20<br>10 10<br>Non-household members<br>0 0<br>1 2 3 4+ 1 2 3 4+<br>Number of household children Number of household children<br>Hours of childcare in last 7 days<br><!-- End of picture text --> The figures show mean childcare hours in the last 7 days by number of household children (ages 0-17). Panel A presents data from RRPS round 4 (January-March 2021) which asks about childcare hours for each household adult, for all children in total, and for all non-household members in total. Previous rounds only ask about childcare hours for the respondent. The hours for ’all household adults’ include the respondent’s hours. Total hours per child is the sum of all childcare hours divided by the number of children.

Panel B presents data for female (black) and male (gray) _respondent_ childcare hours before school closures (dashed lines, recall data from RRPS round 4 about January-early March 2020), during school closures (solid lines, RRPS rounds 1-3 covering May-November 2020), and after schools fully reopened (dash-dot lines, RRPS round 4 covering January-March 2021). Data on childcare hours before and during the school closures period for other care providers are not available.

# reopening.

Respondents provide 30-35 hours a week of childcare. Figure 2 Panel B shows that while female respondents provide around 10-15 hours more than men, men still contribute around 25 hours on average. This contrasts with the image of fathers in African countries as primarily providing economic support and little childcare, but is consistent with recent evidence (Clark, Cotton, and Marteleto 2015; Kah 2012). The gender gap in childcare hours per week increased during school closures—women’s hours increased by 13.4 on average compared to 9.8 for men—but the burden increased significantly for both. This pattern is similar to findings of increases in domestic work 8
2013; World Bank, 2012; Yesuf and Calderon-Margalit, 2013), while others examined one or two indicators for a specific region or city (e.g., Mirkuzie, 2014; Yesuf et al., 2014). In this regard, a World Bank (2012) fact sheet on health equity and financial protection on Ethiopia provides progress on a comprehensive list of health and healthrelated indicators using multiple surveys from different sources.

In this study, we provide evidence on maternal and child health inequalities in Ethiopia. We expand the available empirical evidence on maternal and child health equity in Ethiopia by adding a more detailed inequality analysis using data from a series of comparable surveys from 2000 to 2014. The surveys allow us to analyze the trends in inequalities over a time period that overlaps with most of the MDGs’ years. We also examine the contribution of the underlying socioeconomic determinants of maternal and child health outcomes, such as wealth, place of residence and mothers’ education level.

The rest of the paper is organized as follows. Section 2 presents the methodology, focusing on the indicators and their definition, statistical analysis and data. Section 3 presents the results. Section 4 discusses the results and provides a conclusion.

# **2. Methods**

# _2.1  Indicators and definitions_

Table 1 presents the health status and intervention indicators analyzed in this study. Following recent health equity studies, we identified the indicators based on their relevance to the health MDGs and availability of data (Barros et al., 2012; Victora _et al_ ., 5
yijt = βlog(incomeit) + δXit + γi + ζj + λt + εijt where incomeijt is a rough measure of an individual’s income for period t. Women in the study were asked to recall the amount of money they earned from wage work and from agriculture over a four-month time period.<sup>22</sup> In this model, β represents the elasticity between the previously used measures of risky sexual behavior (unprotected sex, number of partners, and paid sex) and income.<sup>23</sup> We note that we interpret the following results with caution. First, income data using a four-month recall will be very noisy, especially in a questionnaire that was not specifically designed to measure income. Our focus will thus be on the sign of the elasticity as opposed to the magnitude. Secondly, not everyone reports income (213 individuals do not report income), so our analysis will be on a subset of the analytical sample. Finally, while shocks maybe viewed as unexpected and outside of the control of the individual, changes in income may have been foreseen and even planned. We thus use these estimates as supporting evidence of our main result.<sup>24</sup> Table 5 presents the estimated signs of the income elasticities. In Column 1, we see that there is a negative elasticity between income and STIs; in other words, as income increases, women are at less risk for an STI. Somewhat surprisingly, we see a stronger relationship between income and STIs for women in the control arm of the CCT study (Column 2) as well as for married women (Column 3). When we use self-reported sexual behaviors as our outcome of interest, we do not find any significant relationships between income and number of partners or unprotected sex. We do find, however, that there is a negative elasticity between income and paid sex for unmarried women that is significant at the 10% level (Column 15), and that this elasticity is close to zero and different for married women.

> 22 Income was estimated based on the response to these questions:  How much money did you yourself earn during the last 4 months working for wages? and "What are your profits from non-wage farm work?" Profits from non-wage farm work involved asking women to take overall income from agriculture and subtracting the cost of seed and fertilizer.

> 23 The relationship between shocks and income is negative but imprecisely estimated.

> 24 We also find a positive relationship between log income and expenditures (Table A2; Panel B).

15
of education (see Figure 3), male compliers are more advantaged than female compliers in these dimensions as well. Relative to the average SC program student, compliers are more likely to live in medium-sized cities and less likely to live in big cities, without noticeable differences between males and females in this regard.

Overall, then, female compliers are more disadvantaged and less academically proficient than male compliers. Their next-best option may have been a low-quality bachelor’s program, relative to which SC programs may have provided a better match and improved employment outcomes. In contrast, male compliers may have diverted away from good bachelor’s programs and into SC programs that constituted an “undermatch” to their academic preparation and background, thereby worsening their labor market outcomes.

# **5.3 Robustness**

**Different radii.** An identification concern is that all variation might come from students right at, or near, the 10 km radius. If that is the case, our findings might be sensitive to the radius selection. The composition of compliers might change if a larger radius induces a higher share of students to enroll and creates more compliers along the expansion margin. Since a 10-km radius yields a large diversion share, our results on SC enrollment might be interpreted as lower bounds if expansion compliers are more prevalent at other radii.

To assess the robustness of our findings, we estimate the model using different radii values. Figure 4 (top panel) shows that the share of students with available HEIs increases as the radius increases from 5km to 20km (first plot to the left), the share of compliers decreases but remains stable after 10km (middle plot), and the diversion share also increases (top-right plot). These results are expected. For instance, a larger radius of HEI availability raises enrollment costs (e.g., transportation costs to the HEI) and therefore lowers the share of compliers. By the same logic, expansion compliers are more prevalent at smaller radii because enrollment costs are lower. The top-right plot compares the diversion share with and without interactions. The diversion share is less sensitive to the radius choice in the interaction than the no-interaction model.

The bottom panel of Figure 4 displays TSLS estimates of labor market effects for different radii. Despite changes in the share of compliers and the diversion share, estimates are stable. In the case of employment effects (left panel), although the effects change sign for larger radii, they are not statistically significant in any specification. Similarly, estimates for log wages (right) fluctuate in magnitude and significance but remain negative across all radii.<sup>21</sup>

> 21We present the share of compliers and TSLS estimates for different radii for females and males in Figures A1 and A2 in the Appendix. We observe relatively stable estimates across radii values,

17
evidence in Giné et al. (2008), which presents correlates of the determinants of insurance participation using an earlier 2004 household survey. In this earlier study, insurance take-up is found to be decreasing in basis risk between insurance payouts and income fluctuations, increasing in household wealth, and decreasing in the extent to which credit constraints bind, based on self-reported measures of financial constraints as well as proxies such as wealth. This study also finds suggestive evidence consistent with a role for trust and networks; namely, participation in village networks and measures of familiarity with the insurance vendor are strongly correlated with insurance take-up decisions, and risk averse households are found to be less, not more, likely to purchase insurance.

# _B. Self-reported explanations for non-purchase_

As a second source of non-experimental evidence, Table 9 presents household qualitative self-reports, based on our 2006 surveys as well as on the earlier 2004 Andhra Pradesh survey, about the reasons why non-purchasing households did not buy rainfall insurance.

In 2006, the most common single reason cited by households in both samples is “insufficient funds to buy insurance,” with 81% of households in Andhra Pradesh citing it as the most important reason for non-purchase. Explanations relating to the quality of the product, such as “it is not good value” and “it does not pay out when I suffer a loss”, are much less frequently cited by households, and relatively few households cite “do not need insurance” as a reason for non-purchase (2.8% in Andhra Pradesh and 25.2% in Gujarat).

This qualitative evidence appears consistent with our experimental results, where the treatment involving random liquidity shocks has by far the most significant effect on insurance participation rates. The responses appear consistent with the view that liquidity constraints matter significantly for purchase decisions, and also inconsistent with a view that households simply have little interest in insurance against rainfall risk.

Finally, in the Andhra Pradesh sample, a common response to the 2004 survey is “do not understand the product.” The fraction of households citing this reason falls from 21% in 2004 to 2% in 2006, suggesting that households have learned about the policy over time.

# **VI. Improving household risk management: tentative lessons and conclusions**

22
# **Appendix: Data Descriptions and Sources**

|**Variable**|**Definition**|**Source**|
|---|---|---|
|FDI<sup>†</sup>|FDI inflows (constant 2000 USD in<br>million)|UNCTAD World Investment<br>Report online database|
|CFDI<sup>†</sup>|China FDI inflows (constant 2000<br>USD in million)|UNCTAD World Investment<br>Report online database|
|FDIt-1<sup>†</sup>|One year lagged FDI inflows|UNCTAD World Investment<br>Report online database|
|GPCGR|Growth rate of real GDP per capita<br>(%)|World Development Indicator<br>online database|
|GDP<sup>†</sup>|Annual real GDP (USD in million)|World Development Indicator<br>online database|
|WFDI<sup>†</sup>|World FDI outflows (constant 2000<br>USD in million)|UNCTAD World Investment<br>Report online database|
|WAGE<sup>†</sup>|Wage rate in manufacturing sector<br>in US$, converted by official<br>average exchange rates from local<br>currencies|self calculation with<br>International Labor<br>Organization’s LABORSTA<br>and WDI data|
|INCTAX|Corporate income tax rate (%)|PWC_Worldwide Tax_<br>_Summaries_and_European Tax_<br>_Handbook_|
|TARIFF|Taxes on international trade (% of<br>total revenue)|IMF Government Finance<br>Statistics Yearbook and various<br>national statistic agencies|
|OPEN|Ratio of country’s trade turnover to<br>its GDP (%)|self calculation with WDI data|
|ILLIT|Percentage of people aged 15 and<br>above who are illiterate (%)|UNESCO Institute for<br>Statistics|
|PSR|Political stability risk index|ICRG Group|
|TELE<sup>†</sup>|Number of telephone mainlines per<br>1000 people|World Development Indicator<br>online database|

†: variables transformed into logs.

38
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