rafmacalaba/data-use-mentions-extended huggingface dataset:
The probability of a child attending school is positively associated with the household’s education and wealth levels. One additional year of education of the most educated person in the household is associated with a 1.3 percent increase in the probability of school attendance. An increase in one point in the wealth index is associated with a 2.3 percent increase in the probability of an individual attending school.<sup>14</sup> We also include a dummy variable indicating if both parents are permanently living in the household. Children that grow up in households where both parents are present are 9.5 percent more likely to attend school.

Neither urban dummy nor the average value of the wealth index at the cluster level appears as a statistically significant determinant of school attendance, once observed household and individual characteristics are controlled for.<sup>15</sup> This result suggests that the simple correlation between attendance and urban residence reflects individual and household characteristics rather than an overall area effect.<sup>16</sup> To assess the relationship of school supply with attendance we use the 2003 school census, merged with each round of the DHS, only considering the schools that appear to be founded on or before the year in which the survey was taken. We consider a set of variables that approximate the school supply in several dimensions: number of schools, number of classes, total number of teachers, and number of trained teachers. All the variables are divided by the number of children in school age in the locality.<sup>17</sup> These variables are intended to account for both the availability of schools and teachers as well as the quality of the teachers. Additionally, we include in our regressions the average fees charged by schools in the locality. Finally, from the community module in the 1994 and 2000 DHS, we include information on the average time to travel from a central point in the cluster to a primary school.

Table 4 shows results from the analysis of
*4.2: Data Sources**

Key baseline and outcome data come from household surveys. Data were collected in two waves, with the baseline ( _N_ = 3,883, or N=4,484 including the households in the pure control/no-cash condition) occurring about 30 days before the first payments in each district, and the midline ( _N_ = 4,806, or N=6,006 including the households in the pure control/no-cash condition) after about 20 months.<sup>17</sup> The survey featured modules on household consumption, assets, food security, educational attendance and parenting behaviors taken from prior national Demographic and Health Surveys<sup>18</sup> to facilitate comparability for policy purposes.

For child development progress, the Malawi Development Assessment Test (MDAT) pioneered by Gladstone et al. (2010) was adapted to and validated in the local context under the supervision of one of the test’s original creators and a local child development expert in view of its age group applicability and the possibility of relatively close contextual adaptation. The MDAT consists of a series of progressively more difficult activities conducted by a specially trained facilitator with individual children, with separate activity streams to measure skills in the areas of speech/language, fine motor, and social interaction. The traditional Malawi Development Assessment Tool (MDAT) assesses child developmental status across four domains: gross motor skills, fine motor skills, language learning, and social skills, with a composite score that sums across all four domains. There are 136 items, 34 in each domain of development, for use with children 0-71 months of age. In the development of the original MDAT, all items were rigorously tested through an iterative process that allowed the best performing items to be selected. All items use locally sourced materials that are familiar to young Malawian children. The assessor identifies a starting point for the assessment based upon the age of the subject. Each item is then assessed in one of
# _Interviewer characteristics_

The mean values for interviewer characteristics are provided in Table 2. These characteristics are used as covariates in the analytical approach, which is described in the section below.

**Table 2: Interviewer characteristics**<sup>**6**</sup>

|**Interviewer characteristic**|**Value**|
|---|---|
|Female (%)|40|
|Age (mean years)|40.2|
|Lives or has lived in the survey state (Kassala) (%)|50|
|Attended university or higher (%)|77.8|
|Works with CBS (%)|82.4|
|Conducted 10 or more CAPI surveys previously (%)|61.1|
|Self-rated skill with tablets or smartphones before survey (1 – 10) (mean)|9.3|
|“On a scale of 1 to 10, how much do you generally trust other people?” (mean)|6.8|
|“On a scale of 1 to 10, how much do you think other people generally trust you?”|7.6|
|(mean)||

# **3. Analytical approach**

This research seeks to identify interviewer effects and their causes through four different specifications of a model that measures interviewer ICC. Following Brunton-Smith _et al_ . (2016), we use two-level mixed effects linear regression models with respondents nested in interviewers. We deploy four different versions of this model. The first specification simply measures the interviewer ICC for each variable. Specifications II and III aim to control for covariates including interviewer area clusters and the gender of interviewers and respondents. A fourth specification includes interviewer characteristics in an attempt to identify their contribution to interviewer-related measurement error. Note that for each specification, we are only interested in the ICCs that are obtained post-estimation and not the coefficients themselves.

The first specification simply identifies the ICC without
on girls’ goods (especially education), from 1.6% to 2.9%, and the large effects I find on remittances. The latter increase from 6% of household income to about 9.5%, which appears to result primarily from transfers to family members who take care of the couple’s children living outside the household. The large increase in remittances hence seems to mask expenditures on children.

Further analysis suggests that the increase in household expenditures on female assignable goods and remittances is not primarily due to an income effect resulting from the promotion. A back-of-the-envelope calculation shows that the monetary increase in female assignable expenditures and remittances combined is more than two times as large as the increase in personal income. These magnitudes suggest that the selected women are not just able to appropriate additional income they may earn as a result of a promotion, but also that a reallocation of income the women would have earned in the absence of the program is taking place. This indicates that the promotion

> 5Note that ten nominees from the shortlisted comparison group joined the promotion program as replacements for women from the selected group who dropped out of the program. Four of these replacement nominees reported that they had been officially promoted during the household surveys. These replacements are part of the comparison group in the analysis.

> 6I define assignable goods as private goods whose nature allows the researcher to deduce that they are consumed exclusively or primarily by a specific type of household member.

4
Ibid.|Fertilizer<br>coupons|None|None|
|Eligibility criteria|Membership to<br>GUK<sup>e</sup>|Membership to<br>a farmer group<br>operating with<br>Softex (cotton<br>purchasing<br>company)|Maize<br>cultivation,<br>less than 15<br>acres|Ibid.|Land<br>ownership|Membership to<br>a farmer group|Membership to<br>a cotton<br>cooperative|
|Sampling frame|Villages of the<br>Bogra region|Farmer groups<br>of the Houndé<br>region|Villages in<br>Northern<br>Ghana<br>included in<br>the GLSS5+<br>survey|Expansion to<br>villages<br>within 30<br>kilometers of<br>rain gauges|Villages in two<br>districts in<br>Andhra<br>Pradesh,<br>Mahbubnagar<br>and Anantapur|Farmer groups<br>of the Meru<br>region|Cooperatives of<br>the Bougouni<br>region|
|Baseline survey|Yes|Yes|Yes|Ibid.|Yes|Yes|No|
|Sample size|2,300|1,015|385<sup>f</sup>|1406<sup>f</sup>|1,479|780|981|
|Number of clusters|120|80|60|72|45|40|87|

Source: Authors’ elaboration.

> a As of August 2020 (when we registered our study and pre-analysis plan).

> b The published article contains an online appendix with supplementary data. However, the data set only includes the variables analyzed in the original study.

> c Some of the subsidies took the form of rebates.

d Free provision was conditional on purchasing improved seeds
# 1.13. Sensitivity Analysis

In this section, we test the results of the analysis by varying some of the underlying assumptions to ensure that results are not highly sensitive to minor variations. To do so, the saving lives indicators were re-estimated. The assumption underpinning the calculations changed, from the percentage of the population that can access a major hospital within 30 minutes (as in the original calculation), to the percentage that can access emergency care within 60 minutes.

Table 5: Assessing ∆ALFwl changing assumption of access to lifesaving services within 60 minutes

|Dimension - Saving Lives (60 mins)|Case 1|Case 2|Case 3|
|---|---|---|---|
|The percentage of population who can access major hospitals<br>in 60 minuteswithoutthe project under theordinarystate|90.20%|99.50%|90.20%|
|The percentage of population who can access major hospitals<br>in 60 minuteswiththe project under theordinarystate|93.60%|99.50%|90.20%|
|The percentage of population who can access major hospitals<br>in 60 minuteswithoutthe project under thedisruptedstate|79.80%|74.10%|90.20%|
|The percentage of population who can access major hospitals<br>in 60 minuteswiththe project under thedisruptedstate|89.00%|74.10%|90.20%|
|∆ALFl<br>w|77.9%<br>(9,143.23)|50%<br>(33,2035.62)|-|

Comparing the results in Tables 4 and 5, we find that there is a negligible variation in the results even when the time assumption is doubled. This suggests that the results of the analysis are sufficiently robust for project comparison.

33
and Rao Gupta (1997) for a review of the literature; also see Quisumbing et al., (2001). Both the latter studies note that whether and how disadvantaged FHH are is inextricably linked to why they are female headed. Detailed studies for Uganda and Zimbabwe find that households headed by widows are especially impoverished relative to male headed and other female headed households (Appleton, 1996, for Uganda; Horrell and Krishnan, 2007, for Zimbabwe).

Given the discussion in Section 2, one might expect that FHH are not particularly common in Mali. Out of 4,494 households in the 2006 ELIM sample, 179 and 131 in urban and rural areas, respectively, are headed by women. They account for 11.3 percent of the population of urban heads and 4.5 percent of that of rural heads. A vast majority of these female heads are widows ─ 75 percent in urban and 64 percent in rural areas versus less than 1 percent of male heads in both areas. The marital status of the rest of the FHH breaks down as follows: monogamous (11 and 9% in urban and rural areas respectively), polygamous (4 and 22%), divorced or separated (7 and 4%). The first may be left-behind by migrant husbands who may or may not send remittances.<sup>11</sup> The second group is often comprised of women whose husbands live with a co-wife and shares his time between wives living in different locations.

A previous study that examines Mali‘s poverty profile using the 2006 ELIM finds that the poverty rate for FHH is 20 percent lower than for male headed households (MHH) (BackinyYetna et al. 2007). The analysis that follows questions these results. In particular, it argues that an investigation of MHH and FHH in Mali using data from the 2006 ELIM reveals that having a female head per se is not decisive in creating poverty but rather that it is the combination of household and individual characteristics, including female
varies systematically across sectors. Figure 6 compares a sector’s value added (from administrative data) with its contribution to Kenya’s GDP (from national accounts). Manufacturing and business services show the closest alignment between these measures, which suggests that the bulk of economic activity in those two sectors takes place in the formal economy. This pattern is consistent with the fact that both sectors rely predominantly on inputs from other firms and tend to sell to other businesses (Figure A1).

In contrast, downstream sectors closer to final consumers exhibit larger disparities between value added and GDP contributions (Figure A8). This pattern aligns with weaker self-enforcement in consumer-facing sectors inherent to most VAT systems (Pomeranz, 2015; Naritomi, 2019). We observe similar patterns in both the extensive margin of informality (comparing firm counts across data sources, Figure A6) and the intensive margin (comparing formal and informal em15
, we are interested in asset prices on a monthly basis, and to address whether financial recoupling (or greater financial contagion) in the late 2000s was associated with financial globalization. At first sight, frequency and length problems arise with both Lane and Milesi-Ferretti’s (2007) and BoP statistics. Annual foreign stocks from LMF are available until 2007, and are inadequate to analyze asset prices and financial globalization in the late 2000s, particularly at times when the global financial crisis may have significantly modified gross foreign asset positions. On the other hand, while updated BoP statistics are available, they are usually reported on a quarterly or an annual basis. To study whether greater flow intensity generates the monthly asset correlation behind financial recoupling, we need higher frequency data such as the U.S. Treasury’s TIC and EPFR data.

<u>12</u>
25 women, possibly as a result of shorter intervals between births. Women who lost a sibling in 1994 do not differ significantly in the number of living children in the post-genocide period (Fig. 5c). In contrast, for widows the gap in the number of living children becomes larger over time compared to women of other civil status (Fig. 5d).

# **6.3 Robustness tests**

We conduct multiple tests on the robustness of the findings (results available upon request). First, the determinants of fertility are estimated with a Zero-Inflated Poisson model to allow the probability to give birth to the first child to differ across (latent) groups of women.<sup>12</sup> Two variants of the Zero-Inflated Poisson model are estimated, with INUNION and the complete set of independent variables used as inflation variables determining the binary process. In both variants of the Zero-Inflated Poisson model the point estimates of the conflict proxies are of comparable magnitude and level of significance as in the original Poisson estimates.<sup>13</sup> Second, we conduct a placebo genocide test, where we use child deaths and sibling deaths occurring between January 1990 and December 1993 as placebo conflict proxies. None of the placebo proxies significantly influences fertility in the short-term or long-term post-war period. This evidence confirms that our analysis captures fertility effects of the 1994 genocide.

Third, we define CHILDDEATH and SIBLINGDEATH as proportions of the total number of children and siblings born before the genocide, respectively. This allows the probability to lose a child and a sibling to vary with fertility at the time of the genocide and family size. The estimated coefficients are larger in magnitude, while the level of significance is similar.

Fourth, using the 1992 RDHS wave, we compare fertility among Hutu and Tutsi. Of the nationally representative sample of women surveyed in the 1992 RDHS, 8.6 percent are Tutsi.<sup>14</sup> Descriptive statistics point towards differences across both groups: On average
_26_

# **VI. Conclusion**

Using matched climate and firm data covering 154 countries, we documented that the effects of rising temperature are non-linear and uneven across climate zones. Firms in hotter zones experience steeper losses in productivity with increases in temperature compared to firms in relatively colder zones, which tend to register productivity gains. A unit (1<sup>0</sup> _C_ ) increase in WBT in the hottest climate zone of 25.7<sup>0</sup> _C_ and above results in a productivity decline of about 20.8% compared to firms in the coldest climate zone. There is a change in the direction of impact or sign of the main coefficient, indicating a potential inflection point beyond which an increase in temperature has a detrimental impact on firm productivity, which was estimated to be 25 _._ 7<sup>0</sup> _C_ in our sample. In addition, the effects vary not only based on the temperature categories within which firms are located, but also on other factors such as firm size, industry classification, income group and region. For example, large firms, firms in manufacturing and those in low-income countries and hotter climate zones tend to experience the biggest productivity losses due to climate change. Given that many low income countries are in hotter climate zones, the climate change impacts due to higher temperature are further exacerbated by limited capabilities to invest in adaptation. Firms in poorer regions experience the highest productivity losses due to climate change especially those in the hottest temperature categories. This reinforces what we know about climate change reinforcing existing vulnerabilities in the regions of the world that are least capable of responding and adapting to climate change.

This study builds on and makes important contributions to the emerging literature. First, using a globally comparable and standardized survey of firms rather than an individual country presents opportunities for better understanding the potentially heterogeneous and uneven impacts of temperature changes on firm outcomes. Most earlier studies that examined climate change impacts using micro-data relied
Policy Research Working Paper 9830

# **Abstract**

This paper introduces a micro-founded methodological framework to estimate the housing deficit across and within emerging economies. It introduces the Adequate Housing Index, which provides a comparable assessment of adequate housing based on seven adequacy dimensions that are held constant across all countries. The adequacy dimensions were obtained and harmonized from countries’ most recent household expenditure and consumption surveys. The paper documents large differences in housing adequacy across a sample of 64 emerging economies, as well as wide within-country disparities such as across income groups, locations, and occupations. Estimates of the Adequate Housing Index show that across the sample of 64 emerging economies, there is a current housing deficit of 268 million housing units affecting 1.26 billion people. About 26 percent of the current housing stock in these economies is inadequate. The paper further estimates that at least 40 million additional housing units will have to be added by 2030 to provide adequate housing to all and accommodate the growing population and urbanization patterns.

This paper is a product of the Sector Economics and Development Impact Department, International Finance Corporation. It is part of a larger effort by the World Bank Group 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://www. worldbank.org/prwp. T he authors may be contacted at dbehr@ifc.org; khaider@ifc.org; ssingh13@ifc.org; azaman@ifc.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
Table 4: All-India Total Factor Productivity

|Year|Aggregate|Average|Reallocation|
|---|---|---|---|
|1985|0.000|0.000|0.000|
|1986|0.001|0.026|-0.025|
|1987|0.023|0.027|-0.004|
|1988|0.030|0.074|-0.044|
|1989|0.017|0.075|-0.058|
|1990|0.014|0.081|-0.066|
|1991|0.033|0.094|-0.061|
|1992|0.042|0.057|-0.015|
|1993|0.086|0.070|0.016|
|1994|0.061|0.074|-0.013|
|1998|0.160|0.124|0.036|
|1999|0.137|0.131|0.007|
|2000|0.161|0.127|0.033|
|2001|0.176|0.137|0.039|
|2002|0.176|0.158|0.018|
|2003|0.167|0.155|0.012|
|2004|0.190|0.171|0.019|

Total factor productivity (TFP) decompositions for the population of firms, conducted at the allIndia level, using the OP method. “Aggregate” indicates market-share weighted mean productivity, “Average” indicates unweighted mean productivity, and “Reallocation” indicates the covariance between market share and productivity. Source: Authors’ calculations based on Annual Survey of Industries data.

37
survey round remains balanced in terms of observable baseline differences. Given the lack of significant differences in attrition by treatment status, and that attrition does not appear to differentially select firms on observables, we maintain a missing-at-random assumption in our analysis for those attriting.

An important point of context is that the period of our study coincided with a period of rapid general economic growth in Sri Lanka. When we began our study in 2008, per-capita GNI (in constant 2011 PPP international dollars) was 7,598.<sup>7</sup> In May 2009, just before our wage subsidy intervention period began, the 25-year civil war ended, and the Sri Lankan economy grew at 8 to 9 percent per year over the 2010 to 2012 period, with per-capita GNI reaching 10,396 in 2014, the year of our last survey. We are therefore testing the return to additional labor in a growing economy, where firms may be expected to have opportunities to potentially grow.

# **4 Take-up and Who Did They Hire?**

# **4.1 Take-up**

During the eight months the incentive program was active, 60 of the 250 firms offered only the wage subsidy took it up (24 percent). The take-up rates were not statistically different (p=0.622) in the wage subsidy plus savings (24.2%), and for the wage subsidy plus training (21.2%) treatment groups, giving a total of 196 firms that used the subsidy for at least one month. Conditional on using the subsidy, the median firm used it for seven out of the eight possible months and received a total of 24,000 Rs. in subsidy. Only 17 percent of those using the subsidy used it for 4 or fewer months, and 68 percent used it for 6 months or more.

Table 3 examines the correlates of the take-up decision, building on early analysis presented in de Mel et al. (2010) which only had take-up data through
For Egypt, Samari (2021) examined h</mark> ow gender norms and household gender dynamics were modified as a result of male return migration from other Arab countries using data from the 2006 and 2012 Egyptian Labor Market Panel Survey. The study estimated treatment effects for 7,314 married couples in 2006 and 8,992 married couples in 2012, using regression models of gender norms and household decisionmaking and return migration. Egyptian women with spouses who migrated to an Arab country and returned, valued more traditional gender norms. Women in households with a spouse who returned also made fewer household decisions compared to women in non-migrant households. Women with spouses who had migrated multiple times also made fewer household decisions compared to women who had a spouse 9