rafmacalaba/datause-encoder-data huggingface dataset:
variables included in the empirical specification). Infant and neonatal mortality are higher among boys and mothers who are poor, have no formal schooling, or reside in rural areas. Poverty is defined as having none of the following durable goods: radio, TV, refrigerator, bicycle, motorcycle/scooter, car/truck.<sup>6</sup> When controlling for mother fixed effects the impact of debt relief is identified by the mothers who give birth both before and after the year of the debt relief settlement. In the sample, there are 115,837 such mothers (approximately 23 percent of all mothers) for Decision Point and 110,378 mothers (approximately 22 percent of all mothers) for Completion Point. I test if these women are significantly different from the women who only give birth before or after the different stages of debt relief in terms of age, poverty, educational status, and rural residency. The evidence on this is presented in Table 4. Since age is significant (first column in Table 4), the regressions on poverty, educational status, and rural residency in columns (2) to (4) apply country-age fixed effects to compare women of different countries at the same age. Compared with mothers who only give birth before or only after the different stages of debt relief, the mothers giving birth on both sides of debt relief are older, less educated (no formal schooling), more likely to reside in rural areas, and poorer. Thus, results on debt relief presented in Section 4 may be indicative of which effects the HIPC Initiative has on more vulnerable women who fulfill these criteria. The sensitivity analyses in Section 4.2 give further discussions and tests on how debt relief may affect the sample of women.

# **3.2 The Empirical Model**

I apply a linear probability model to investigate the relationship between debt relief and infant mortality. Since the error terms are likely to be autocorrelated, the fixed effects logit model is not an appropriate alternative and the coefficient estimates of the linear model have a more straightforward interpretation (Cameron & Trivedi, 2005). The model is presented below, where the dependent variable _In fant Mortalityimacy_ is a dummy which equals one if baby _i_ , born in country _c_ to mother _m_ , born in mother’s birth cohort (five-year) _a_ , in year _y_ , dies before reaching one year of age. In some specifications, this variable is replaced by _Neonatal Mortalityimacy_ , which equals one in case of death before the baby reaches one month of age. _Debt Reliefcy_ is a dummy variable equal to one if country _c_ has reached Decision Point or Completion Point of debt relief in year _y_ . For example,

> 6The poverty, education, and residency indicators give the status at the time of the interview. Pairwise correlations between the variables suggest little overlap: Poor and No formal schooling: 14 percent, Poor and Rural: 19 percent, and No formal schooling and Rural: 23 percent.

8
23 average (instead of the observed 3 years)-equivalent to completing primary schooling-growth in consumption would have been 2 percentage points higher. A difference of one standard deviation in terms of initial asset value (about half of which is accounted for by land) put households on a 2 percentage point higher consumption growth path. Households which in 1992 were afflicted by health problems-related to malaria in over 80 percent of cases-experienced consumption growth which was (other things constant) 1.8 percentage points lower than those not experiencing such problems. Households with access to electricity enjoyed consumption growth that was 6 percentage points higher than other households.

The above results offer insight into what determined the growth in income and consumption among Ugandan households. How did such growth affect poverty? To address this, Deininger and Okidi estimate a multinomial logit model of changes in poverty status (households are classified as either not changing their status, falling into poverty or escaping from poverty). They find that the relative coffee price changes had a powerful poverty-reducing impact, indicating that their effect was broad-based and that price changes in tradable commodities directly benefited poor producers (and not simply indirectly through the labor market.) Moreover, households with higher education, more initial assets (land), better health, and better access to infrastructure (electricity) and location (distance to municipality) were far less likely than others to fall into poverty, and more likely to escape from it.

These results from these micro-econometric analyses of panel data point to the following factors that appear to influence the relationship between economic growth and poverty reduction:

- First, many rural households stand to benefit directly from liberalization measures, as well as increased political stability and better governance. And the gains can be substantial. In so far as liberalization measures increase producer prices, rural producers will gain, and to the extent that food marketing margins tend to decline, rural consumers will gain as well. Nonetheless, some will gain more than others, depending on the product- and consumption-mix of the household.

- Second, a household's location is also key in conditioning the extent to which it will benefit from liberalization measures. Specifically, whether the household had access to infrastructure and urban markets was an immensely important factor in governing the growth in household income. It explains about half of household consumption growth and poverty reduction in Ethiopia during 1989-95, and it was also quantitatively
affected. We capture this effect using a dummy variable equal to 1 if the share of women workers in the firm prior to the pandemic was more than 25 percent and 0 otherwise (High share of women workers). The data source is ES.

We also consider the financial condition of the firm. Studies find that a stronger financial condition (more cash, less debt, and better access to finance) prior to the pandemic helped firms perform better during the pandemic (Amin and Viganola 2023, Ding et al. 2021). Since many SMEs may obtain finance from non-bank financial institutions, our main measure of a firm’s financial constraints is a broad one. This equals the pre-pandemic severity level (on a 0–4 scale) reported by the firms of access to finance as an obstacle to their operations (Finance obstacle). The second measure focuses on bank finance. It is equal to 1 if the firm had an overdraft facility prior to the pandemic and 0 otherwise (Overdraft). The data source for the two finance variables is ES. The age of the firm and location (urban vs. rural) have been found to impact the firm’s performance during the pandemic. Using data on over 4,000 firms in Spain, Fernández-Cerezo et al. (2022) report a much larger decline in sales due to the pandemic among younger firms and firms located in urban vs. rural areas. Age may matter because younger firms are less experienced and have fewer resources to deal with the pandemic. Younger firms are also disadvantaged because they have a less developed supplier network and a less dedicated customer base. Urban areas may be more susceptible to the virus because of greater contact between people due to higher population density, more crowded housing, and more interconnected public transport. Thus, we account for a dummy variable equal to 1 if the firm is 10 years old or younger (in 2020) and 0 otherwise (Young firm), and a dummy variable equal to 1 if the firm is in the country’s capital city and 0 otherwise (Capital city). The data source for both variables is ES.

14
## **Table 6. Conditional correlations between digital occupation level and cognitive and socioemotional skills requirements in Malaysia**

_LPM regressions of digital occupation levels on dummies taking the value of one if the skills count of a skills category of an occupation is above the mean_

|||Low vs.|Occup<br>|ation digita<br>|l level<br>|High vs.|
|---|---|---|---|---|---|---|
|||<br>Very<br>low|Medium<br>vs. Low|High vs.<br>Medium|High vs.<br>Low|<br>Very<br>low|
|||(1)|(2)|(3)|(4)|(5)|
|Cognitive|Thinking|**0.36***|0.22|0.01|**0.53***|**2.09*****|
|||(0.17)|(0.13)|(0.15)|(0.23)|(0.40)|
||Communication|**0.47***|-0.19|0.01|-0.17|-0.09|
|||(0.21)|(0.14)|(0.33)|(0.24)|(0.16)|
||Organization|0.00|-0.16|0.12|-0.13|-0.60|
|||(0.16)|(0.15)|(0.10)|(0.14)|(0.41)|
|Socioemotional|Emotions|-0.00|**0.48****|-0.30|0.12|0.81|
|||(0.15)|(0.15)|(0.21)|(0.19)|(0.41)|
||Relationships|**-0.30***|-0.35|0.08|**-0.58***|-0.67|
|||(0.14)|(0.18)|(0.38)|(0.24)|(0.43)|
||Personal growth|-0.16|-0.03|**0.36***|0.34|-0.55|
|||(0.16)|(0.17)|(0.18)|(0.20)|(0.41)|
||Constant|0.44**|1.47***|2.22***|1.39***|0.74|
|||(0.14)|(0.14)|(0.22)|(0.33)|(0.54)|
||N|79|75|47|54|51|
||R-sq|0.36|0.36|0.13|0.15|0.44|

_Source_ : Authors’ calculations based SEAD data.

_Note_ : The regressors for the outcome are dummy variables taking the value of one if the skills count of a skills category of an occupation is above the mean. The 2017 Malaysian labor force survey does not report any workers in one of the 127 occupations from our occupational skills profile (paramedical practitioners, ISCO code 224).

_*p < 0.1 **p < 0.05 ***p < 0.01_

# **6. Complementarities among Digital, Cognitive, and Socioemotional Skills: The Case of Malaysia**

Cognitive and socioemotional skills correlate more strongly with basic digital skills than with intermediate and advanced digital skills in Malaysia. Basic digital skills correlate highly with all three subsets of cognitive skills and with one socioemotional skill in Malaysia. The correlation coefficient between basic digital skills and thinking, communication, organization (cognitive), and emotions (socioemotional) is around 0.5 (Table 7). Basic digital skills are not correlated with relationships or personal growth in Malaysia. In contrast, intermediate digital skills correlate with thinking and 26
**Table 8: Impact of a change in the price of cereals on consumers- National**

||**-25%**|**-12.5%**|**No change **|**12.5%**|**25%**|**50%**|**100%**|
|---|---|---|---|---|---|---|---|
|||||**Rice**||||
|**Poverty, population as a whole**||||||||
|Headcount index of poverty|46.09|46.62|**47.45**|48.18|48.93|50.22|53.02|
|Poverty gap|16.28|16.46|**16.66**|16.87|17.10|17.62|18.82|
|Squared poverty gap|7.83|7.92|**8.01**|8.11|8.21|8.45|9.07|
|**Poverty, rice consumers**||||||||
|Headcount index of poverty|42.31|42.97|**43.99**|44.88|45.81|47.39|50.84|
|Poverty gap|13.82|14.05|**14.29**|14.55|14.84|15.47|16.95|
|Squared poverty gap|6.22|6.32|**6.43**|6.55|6.68|6.98|7.75|
||||**Millet a**|**nd Sorghu**|**m**|||
|**Poverty, population as a whole**||||||||
|Headcount index of poverty|46.56|46.91|**47.45**|47.99|48.22|49.36|51.23|
|<br>Poverty gap|16.36|16.50|**16.66**|16.82|16.99|17.36|18.25|
|Squared poverty gap|7.86|7.93|**8.01**|8.09|8.18|8.37|8.85|
|**Poverty, millet and sorghum consumers**||||||||
|Headcount index of poverty|34.95|35.51|**36.37**|37.23|37.61|39.43|42.43|
|Poverty gap|10.39|10.63|**10.87**|11.13|11.41|12.00|13.42|
|Squared poverty gap|4.38|4.50|**4.62**|4.75|4.89|5.20|5.97|
|||||**Corn**||||
|**Poverty, population as a whole**||||||||
|Headcount index of poverty|47.41|47.45|**47.45**|47.51|47.51|47.58|47.90|
|Poverty gap|16.61|16.63|**16.66**|16.68|16.70|16.75|16.86|
|Squared poverty gap|7.98|8.00|**8.01**|8.02|8.04|8.06|8.13|
|**Poverty, corn consumers**||||||||
|Headcount index of poverty|35.60|35.80|**35.80**|36.09|36.09|36.46|38.03|
|Poverty gap|11.28|11.40|**11.52**|11.63|11.75|12.00|12.53|
|Squared poverty gap|5.15|5.21|**5.28**|5.35|5.42|5.56|5.88|
||||**Whea**|**t and bread**||||
|**Poverty, population as a whole**||||||||
|Headcount index of poverty|47.03|47.29|**47.45**|47.53|47.61|47.76|48.33|
|Poverty gap|16.56|16.61|**16.66**|16.70|16.75|16.86|17.07|
|Squared poverty gap|7.96|7.98|**8.01**|8.03|8.06|8.12|8.23|
|**Poverty, wheat and bread consumers**||||||||
|Headcount index of poverty|43.02|43.37|**43.59**|43.69|43.80|44.00|44.78|
|<br>Poverty gap|14.68|14.74|**14.81**|14.87|14.94|15.08|15.37|
|Squared poverty gap|6.95|6.99|**7.02**|7.06|7.09|7.17|7.32|
||||**All iden**|**tified cerea**|**ls**|||
|**Poverty, population as a whole**||||||||
|<br>Headcount index of poverty|44.85|46.05|**47.45**|48.84|50.09|52.79|58.41|
|Poverty gap|15.87|16.24|**16.66**|17.11|17.62|18.76|21.67|
|Squared poverty gap|7.62|7.80|**8.01**|8.23|8.48|9.05|10.68|
|**Poverty, food consumers**||||||||
|Headcount index of poverty|43.34|44.59|**46.05**|47.51|48.80|51.63|57.49|
|Poverty gap|15.01|15.40|**15.83**|16.31|16.84|18.03|21.06|
|Squaredpoverty gap|7.07|7.26|**7.47**|7.71|7.96|8.57|10.26|

Source: Authors’ estimation using ELIM 2006.
- 16 -

Table 7 Share of Various Traditional and Non-traditonal Earmarkina Sources in Total Earmarking. 1980-85 a/

||1980|'981|1982|1983|1984|1985|
|---|---|---|---|---|---|---|
|Natianal|72.5|71.6|75.2|72.5|75.4|73.3|
|Traditional Earmarking|44.6|43.7|40.6|37.6|35.4|36.3|
|Tax on Imports C.I.F.|4.9|4.4|4.5|3.5|4.1|4.1|
|Gasoline Tax|12.5|12.3|9.6|7.9|6.5|7.2|
|Tax on International|||||||
|Travel|0.7|0.9|1.0|0.9|0.7|0.7|
|Coffee Export Tax|2.7|1.9|1.4|1.3|1.3|1.6|
|Tax on Tourism|0.3|0.3|0.3|0.3|0.1|0.0|
|Tax on Movie Tickets|0.0|0.0|0.0|0.0|0.0|0.0|
|Contributions and|||||||
|Participation|0.9|0.6|0.8|1.0|0.9|1.5|
|Special Funds|0.0|0.0|0.0|0.2|0.3|0.6|
|Revenue Sharing|21.6|22.1|21.9|21.6|20.7|19.9|
|Tax Allowance|14.3|16.1|**15.0**|15.5|13.8|12.5|
|Sales Tax|7.3|6.0|6.9|6.1|6.9|7.4|
|Other|1.1|1.2|1.0|1.1|0.9|0.8|
|Non-Traditional Earmarking|27.9|27.9|34.6|34.9|40.0|37.0|
|Payroll Taxes|22.0|23.2|21.6|25.3|23.4|22.2|
|Social Security|16.4|17.6|16.0|19.5|17.9|17.1|
|SENA, ICBF, etc.|5.6|5.6|5.6|5.8|5.5|5.1|
|Operating Profits|||||||
|<br>of Non-Financial|||||||
|Public Enterprises|5.9|4.7|13.0|9.6|16.6|14.8|
|Departments|18.2|17.3|14.4|15.9|14.2|14.1|
|Traditional Earmarking|'4.1|13.5|11.1|12.3|11.3|11.0|
|Non-Traditonal Earmarkin&|||||||
|Social Security|1.1|1.2|1.0|1.1|0.7|1.1|
|Operating Profits of<br>Non-Financial|||||||
|Public Enterprises|3.0|2.6|2.3|2.5|2.2|2.0|
|Municipal|9.4|11.1|10.4|11.5|10.4|12.6|
|Traditional Earmarking|2.7|2.6|2.2|2.8|2.2|1.8|
|<br>Non-Traditional Earmarking|||||||
|Social Security|0.5|0.5|0.5|0.7|0.6|0.6|
|Operating Profits<br>of Non-Financial|||||||
|Public Enterprises|6.2|8.0|7.7|8.0|7.6|10.2|
|Total|100.0|100.0|100.0|100.0|100.0|100.0|
|Traditional Earmarking|||||||
|(All Levels)|61.4|59.8|53.9|52.7|49.0|49.1|
|Non-Traditional Earmarking|||||||
|<br>(All Levels)|38.6|40.2|46.1|47.3|51.0|50.9|

**a.** See footnote to Table 6 for definitions.

Source: Estimates based on **data** in Table A2 in Statistical Appendix.
some of the most variable climates on the world, with the predominant disasters being droughts (Brown and Crawford 2008) and floods (Armah et al. 2010). We use the latest available Ghana Living Standard Survey (GLSS V) 2005, to estimate the impact of remittances on ex ante preparedness of households. Of the 8687 households in the sample, 2181 households (25 percent) receive domestic remittances, while 541 (6.5 percent) receive remittances from OECD countries and 122 (1.5 percent) receive remittances from African countries (see annex table 2). Since we can identify the source of remittances, we can distinguish the differential impact of remittances from relatively richer OECD countries and poorer African countries on the receiving households. However endogeneity of remittance-receiving status needs to be controlled for in our analysis. As in the previous section, we used propensity score matching to construct comparable households on the basis of observable household characteristics.

Materials used for the construction of the house potentially reveal how prepared households are in the event of disasters such as flood, earthquakes, cyclones and landslides. Concrete houses are usually more disaster resilient, while houses made of mud and bricks are more susceptible to destruction in the event of a disaster. Ghanaian households that receive international remittances from OECD countries are more likely to have a concrete house. Without controlling for endogeneity of the remittance-receiving decision, 44 percent of Ghanaian households that do not receive remittances have a concrete house. 49 percent of households that receive remittances from other African countries have a concrete house and 77 percent of households that receive remittances from OECD countries have a concrete house.

After controlling for endogeneity of remittance-receiving status, 77 percent of Ghanaian households that receive remittances from OECD countries have a concrete house versus 68 percent of comparable households that do not receive remittances (see figure 3 and annex table 3). Of households that receive remittances from other African countries, 49 percent have a concrete house, versus 45.3 percent of comparable households that do not receive remittances.

As shown in figure 3, even after correcting for endogeneity of remittancereceiving status, households that receive remittances from OECD countries and those that receive remittances from other African countries have fewer mud houses. Similarly, remittance-receiving households have roof made of corrugated iron sheets, cement, concrete, asbestos, slate and roofing tiles rather than roofing material made of leaves.

13
(Cullen, 2023).<sup>14</sup> We also measured impacts on secondary outcomes suggestive of mechanisms. For conceptually linked outcomes measured with multiple questions, we constructed summary indices following Anderson (2008) and our pre-analysis plan, to minimize risks from multiple hypothesis testing. For other related outcomes, where more appropriate, we summed the number of agreed statements within a category (e.g., opinion about situations where wife-beating is acceptable) or reported individual question scores.

# **3.3 Sample Characteristics**

Table A3 shows baseline balance across treatment groups, with no normalized differences exceeding 0.25 standard deviations, indicating good balance (Imbens and Rubin, 2015). Men and women averaged 40 and 36 years old with 4.7 and 4.5 years of education respectively. Half the sample was legally married, with others cohabiting. Couples had an average of 3.7 children. Baseline IPV rates were high: in the preceding 12 months, 49% of women reported emotional violence, 40% physical violence, and 42% sexual violence.

# **3.4 Program Take-Up and Compliance to Treatment Assignment**

Program take-up and attendance were high: 95% of couples offered a place attended the training, completing an average of 21 of 22 sessions.

As treatment assignment was based on VSLA membership, some couples belonged to VSLAs in villages different from their official residence. This is common in Rwanda’s densely populated context, where VSLA membership is not restricted by village boundaries. We accounted for this VSLA-village structure by implementing geographic buffers between study villages, minimizing the extent of this partial ‘compliance’.<sup>15</sup> Our results are robust to various specifications accounting for this geographic distribution (tables A4, A5, A6).

> 14Cullen (2023) found ACASI elicited marginally higher reports of sexual violence than face-to-face methods in this context, though lower than list experiments, which were not used here due to power and reliability concerns (Chuang et al., 2021; Cullen, 2023).

> 15Over 70% of control couples had no treated couples within 600 meters of their dwelling (a significant distance given the region’s terrain), rising to 90% for a 200-meter radius (the radial densities are shown in figure A1).

13
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> in the non-farm participation decision.1<sup>7</sup> Moreover, current assets of the household are also likely to be endogenous to the occupations of the household members. To deal with the endogeneity problem, we employ the Two-Stage Conditional Maximum Likelihood (TSCMLE) approach.

# IV. The Data

The data for our analysis come from the Nepal Living Standard Survey (NLSS) 1995/96. The NLSS consists of a nationally representative sample of 274 primary sampling unit (PSUs) selected with probability proportionate to population size, covering 73 of the 75 districts in Nepal. In each of the PSUs, 12 households were also selected randomly (16 households in the Mountain regions) providing a total sample size of 3373 households. With an average household size of about 5.6, the survey collected detail information for 18855 individuals. The NLSS is unique in the sense that it contained an entire section of questionnaire on parental information, including level of education, sector of employment and place of birth. The survey contains detail information on employment by sectors and by occupations at individual levels. However, for those parents who do not live in the household, or who are deceased, only four types of employment status were recorded; whether they were employed as wage labor in agriculture or non-agriculture, or self employed either in agriculture or non-agriculture. Hence the sample does not allow finer analysis of occupational followings among children. As an individual is allowed to record more than one types of employment at the same time, the employment status data can be taken as if indicating whether the person was ever employed in non-farm sector. However, data on time spent on different activities are not available, precluding any attempt to distinguish between primary and secondary occupations. As regression analysis reported in the subsequent section indicate, the basic results on intergenerational correlations in occupation remain unchanged whether we focus on the sample that includes observations reporting multiple occupations or on the sample which focuses only on those reporting a single occupation.

> Of the total individual level sample of 18347 for whom parents can be identified from the data, nearly 71 percent reported participation in the labor force, but about 20 percent did not report

> "<sup>7</sup> Observe that even though the education level is predetermined when the occupational choice is made, it can not be treated as exogenous, assuming that the value of education derives from future labor market. Of course, as discussed earlier, in case of girls, the endogeneity might not be important if the value of education derives from assortative matching in marriage market.

10