rafmacalaba/data-use-mentions-extended huggingface dataset:
TEGRA provided data on nine reading domains; a list of these is included in the annex. An average reading score is used to measure the overall effect of the CLRW intervention. It was defined as the average of the standardized scores of the nine reading domains. The reading domains were standardized using the mean and standard deviation of the baseline sample for each particular grade and round of survey, as some reading domains were not comparable between rounds. An index of home support was also used in this analysis and defined as the average for the 7 measures of home support. These measures of home support were whether a student receives help with his or her homework, whether parents are interested in his or her school day, whether he or she reads aloud at home, whether someone reads to the student at home, whether the child has been absent in the past two weeks of school, and whether he or she attended preschool and a CPBA. Note that the student is the respondent to these questions and is typically 6- or 7-year-old; it is not clear how reliable their responses were.

Three samples were used to estimate the effects of the CLRW intervention. The first sample was used to measure the average effect of one year of exposure to the intervention. This consisted of 1<sup>st</sup> grade students in 2016 and 2017. Both cohorts were weighted equally to provide an average effect. 1<sup>st</sup> grade students in 2015 were excluded from this because no data were collected at the beginning or end of the 2015 school year. The second sample was used to measure the effect of two years of exposure. This consisted of students who started 1<sup>st</sup> grade in 2016. The third sample was used to measure the persistence of the CLRW intervention’s effect on reading skills. This consisted of students who were in 1<sup>st</sup> grade in 2016 but compared their test scores at the end of
The model can be linearized by applying the natural logarithm and estimated as follows: The ratio Pr(Yi=j) is known as the odds ratio or relative risk. Pr(Yi=J)

**The effect of each explanatory variable or factor on the relative risk is called the relative risk ratio (RRR)** It corresponds to the exponential function evaluated in the coefficient associated with the explanatory variable in question. For continuous explanatory variables, the RRR represents the change in the likelihood that the dependent variable is in a particular category relative to the reference category, associated with a one-unit change in the corresponding independent variable. In the case of dichotomous variables, the RRR represents the change in the probability that the dependent variable is in a particular category with respect to the reference category, associated with exposure to the dichotomous variable or factor.

**Assuming that the variable** xik **is a dichotomous variable, the effect of** xik **on the relative probability is shown in Equation 8.** The interpretation of the relative risk ratio is as follows. A RRR of 1 indicates that exposure to the factor does not affect the relative probability. A RRR greater than 1 indicates a positive association and that exposure to the factor increases the relative probability of obtaining the outcome. A RRR of less than 1 indicates a negative association and that exposure to the factor decreases the relative probability of getting the outcome.

# 5.2.2. Empirical strategy and explanatory variables

**The multinomial logistic regression allows analyzing individual factors affecting the propensity to migrate and accounting for different reasons for migration.** Analyzing the migration decision from a micro perspective is essential to understand the differences in individual and household characteristics that determine why some people decide to move while others stay. It also allows understanding the differences in individual and household characteristics that encourage different reasons for internal migration.

**The Census allows for the identification of internal migrants during the period 2015-2020, and it also provides information on
existing nationally representative surveys were used as a sample frame, the respondents tend to be household heads and are therefore more likely to be male and older. As a result, indicators measured at the individual level, such as job type, are likely to be biased and differ from other nationally representative surveys.

**The HFPS includes a range of questions related to the household and to the individual responding to the survey** . Questions about the household include household characteristics and behaviors related to health, managing COVID-19 protocol, household income changes, education decisions, and strategies to manage income loss, among others. The variables that are most relevant for this paper are: urban status, ownership of a household enterprise, prevalence of income declines or increases, and coping strategies employed by the household. Urban status is determined based on the location of the household in the baseline survey and verified by the enumerator. Notably, the data set does not include a household roster or the work activity of its individual members. A single respondent per household provides information on behalf of the entire household. The respondent also answered basic demographic questions about himself or herself (age, sex) as well as his or her work activity (self-employed, worked in a family business, wage employee, temporary employee). The respondent was not asked about his or her wages or earnings.

**We construct and use two alternative definitions to identify urban households that own enterprises in the HFPS data** . **The first definition uses household-level variables.** We generate a variable that takes a value of 1 if (i) anyone in the household owns a business,<sup>7</sup> and (ii) the household received farm or nonfarm enterprise income during the 12 months prior to the survey. We call this the “household business definition.” This definition allows us to differentiate between farm and non-farm entities, which are both relevant in urban SSA. We thus can classify a household into one of four
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On the health side, the sample data come from the two DHS data in 2011 and 2018. After data with missing values are excluded, data for about 38,000 individuals are used, of which about 5,800 people reported that they had a fever within the last two weeks. On the transport side, travel time is estimated by using spatial analysis based on the underlying road conditions. Using road condition data (i.e., road roughness), network analysis was carried out to estimate the shortest travel time required to visit a health facility. In general, it takes more time to drive on roads in poor condition. Other connectivity variables, such as estimated transport costs and road distance, which are used in the robustness discussion, are also calculated in the same manner. These variables are all related to the underlying road conditions but different. The average travel time to visit a health facility is 0.14 hour i.e., about 10 minutes ( **Table 2** ). However, there is a substantial variation from nearly zero minutes to more than 2 hours.

The information on whether a household owns transport means is also included in the model. In general, transport means are conducive to better access of households to health facilities. In our data, about 38 percent of households possess bikes or motorcycles. There are only a few households that own cars.

Whether people visit a health facility may also depend on what kind of facility exists around them. The model includes the type of health facility, following Mozambique’s health system classification: Hospital, urban and rural health centers, and health posts. Since remoteness may be a general disadvantage of health facilities in terms of medical supplies and availability of doctors and nurses, the distance from each facility to the nearest major city is calculated and included in the model. The major cities with more than100,000 population are considered, and the distance is measured by travel time by car.

In addition
while average consumption among the rural poor increased by 1-percent per year.<sup>2</sup> At the same time, poverty significantly increased in urban areas, from 42.2 percent in 2012 to 55.5 percent in 2022.

The Demographic and Health Survey, carried out in 2021, spanned all nine provinces and 22 regions. Similarly to the EPM survey, we can use the DHS data to draw inferences at the sub-regional level. It entailed the surveying of a total of 20,510 households, comprising 5,146 in urban areas and 15,364 in rural areas. All women aged 15-49 living in the selected households or present the night before the survey were eligible for interviews. Additionally, a subsample of every other household included the weighing and measuring of all children under 5 years to assess their nutritional status (including wasting, underweight, and stunting). In this subsample, all children aged 6-59 months were eligible for anemia and malaria (RDT) tests. Furthermore, anthropometric measurements and anemia testing were also conducted for all women aged 15-49 in this subsample. Finally, in the other subsample of every other household, all men aged 15-59 were eligible for interviews, and gender-based violence questions were administered to one woman per household, randomly selected.

The 2021 DHS shows a modest improvement of stunting rate at 40% compared to 41.6% in 2018. In rural areas stunting declined from 42.6% to 40.5%, and in urban areas it also dropped from 37.7% to 35.5%. This suggests a positive trend in reducing child malnutrition, though there is still a considerable disparity between rural and urban regions, highlighting the need for continued efforts in addressing this issue, particularly in rural areas.

> 2 Approximately 80 percent of Madagascar’s population dwells in rural areas.

6 | P a g e
column (v) of Table 8) are all similar to results in column (iv). There are several notable differences in results using Samples 3 and 4. First, institutional capacity is significant in column (v). Second, creditworthiness is insignificant. Third, the transition dummy is positive and significant. Fourth, we add a dummy for observations over the IDA income cutoff.<sup>34</sup> Per-capita IBRD borrowing is higher for these observations than for those below the IDA cutoff, even when controlling for the volume of IDA borrowing.

Overall, the similarities in results for borrowing and graduation outweigh the differences, and the same variables that predict a greater probability of graduation also tend to predict reduced likelihood of borrowing from IBRD and reduced amounts borrowed. Because the most likely graduates—high-income countries with strong institutions and credit ratings—are also attractive low-risk borrowers from the standpoint of the Bank’s balance sheet, it is unlikely that the reduction in borrowing is driven by unwillingness of the Bank to lend to these countries. Therefore, these results are consistent with a graduation model that is evolutionary rather than abrupt: the same factors that gradually reduce a country’s demand for IBRD loans also eventually lead it to graduate.

> 33 Fixed-effects tobit models (similarly to probit) cannot be estimated using conditional maximum likelihood, and unconditional fixed-effects tobit estimates are biased.

> 34 Data on the IDA operational cutoff threshold begin only in 1987, so the period covered is 1987-2008.

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linked to the census at the enumeration area level. The present analysis is limited to 65 rayons, as it was not possible to include the rayons in the occupied zone.

# **2.1 The imputation process in brief**

HBS data are used to estimate a prediction model for consumption, employing a set of explanatory variables which are common to both the survey and the census. The parameters from this model are then applied to census data to derive an imputed value for consumption. This then allows us to derive a set of welfare indicators based upon consumption, such as headcount poverty. Finally, these welfare indicators are constructed for geographically defined subgroups of the population.

The procedure is implemented in three stages. In the first stage we select a set of variables common to the survey and the census. The choice of variables is informed by the comparability of the question wording between the census and HBS questionnaires. Only where it is clear that their ‘meaning’ is the same in the two data sources are variables selected. The choice is further refined by then comparing the distribution of the variable in the two sources and restricted to only those variables where there is no significant difference in the average (mean) value between the two. Only those variables which are found to be comparable on both these counts are then considered in the second stage.

In the second stage, using the HBS and the variables selected in the first stage, we derive a model for predicting the household consumption. In the final stage, the model of consumption derived in the second stage is applied to the census data set (see Technical Note 1 for a detailed description of this methodology).

One of the key assumptions of this technique is that the census and the survey represent the same population. For this reason it is important to choose a household budget survey which is collected around the year of the census data collection.

4
number of workers who provided family labor on each plot during the last major/minor season.<sup>4</sup> An example of the latter approach is the 2014/15 Tanzania National Panel Survey (NPS), which asks “during the long rainy season 2014, how many days did [NAME] spend on the following activities on this [PLOT]?” This is followed by a question about the “typical number of hours per day worked by an individual on this [PLOT]”. Many other agricultural surveys follow variants of this latter approach,<sup>5</sup> including the 2015/16 Ghana agricultural labor survey used in this study.

A major advantage of collecting data on farm labor by person and by plot (denoted here as _person-plot level_ ) is that it accommodate different analytic approaches at the analysis stage. Table A1 (appendix) shows for several recent studies the main unit at which farm labor is reported and/or embedded in the empirical analysis. Studies concerned with gender differences in agriculture tend to aggregate at the plot-level (sometimes with normalization for plot size), because this allows to compare levels of productivity between plots managed by male and female farmers and assess the role of labor shortages as one of the underlying constraints (Aguilar et al. 2016; Ali et al. 2016; Fisher and Kandiwa 2014; Kilic et al. 2015; McCarthy et al. 2016; Oseni et al. 2016; O’Sullivan et al. 2014; Palacios-Lopez and Lopez 2015). Other analyses of agricultural productivity aggregate either at the plot-level (Gollin and Udry 2017) or at the household-level (Andre et al. 2017; Dillon et al. 2017; Restuccia and Santaeulalia-Llopis 2017). McCullough (2017) aggregates farm labor at the person-level to compare labor productivity between farm and non-farm household enterprises. This illustrates that different levels of aggregation serve different purposes. Collecting data on farm labor at the person-plot level keeps all higher levels of aggregation – i.
resulting two treatments (individual benefit and household benefit) are in Table 1.

**Table 1: Design of randomized survey experiment**

|**Group**|**Question**|
|---|---|
|**Control**|Are you willing to get a COVID-19 vaccine?|
|**Individual Beneft**|The best choice you can make to protect yourself from|
||COVID-19 is to get fully vaccinated. This greatly reduces the|
||risk of severe illness, hospitalisation and dying from COVID-19.<br>Are you willing to get a COVID-19 vaccine?|
|**Household Beneft**|The best choice you can make to protect your household|
||and family from COVID-19 is to get fully vaccinated. This will|
||help to keep the people you love healthy, safe and strong.|
||Are you willing to get a COVID-19 vaccine?|

In total, 10,169 people completed the online survey, of which 2,937 people took part in the online experiment. As the experiment focused on ways to increase willingness to get vaccinated, only people who had not received a COVID-19 vaccine were eligible (i.e., all respondents to the Facebook advertisement who stated they were already vaccinated for COVID-19 did not participate in the experiment). Survey experiment participants were randomly allocated into three groups (the two treatment groups and the control group). Forty percent of participants were allocated to the control group and the remaining 60 percent were split between the two treatment groups. The sample sizes for the treatment and control groups were informed by best practice in the literature (Haaland et al., 2022). Randomization ensured there were very few statistically significant differences between treatment and control groups across demographic characteristics (see Table A4.1 in Appendix). As such, we present graphs showing treatment effects by comparing means across groups (with survey weights) in the body of this
5.0<br>0.0 0.0<br>Percentage<br>Percentage<br><!-- End of picture text --> Source: Own estimates based on Population and Housing Census 2020.

Source: Own estimates based on Population and Housing Census 2020.

**There are no significant differences across migration status in the percentage of women that are household heads.** Indeed, approximately 19 percent of both women who migrate and women who did not migrate are heads of household. On the other hand, migrant men are more likely to be household heads than those who did not migrate (45 versus 42 percent).

**Marital status significantly differs among women who migrated compared to those that stayed in the same state.** Almost 35 percent of women who did not migrate are married against 26.1 percent of women who migrated internally. Around 29.7 percent of migrant women are cohabiting,<sup>7</sup> while this percentage is only 17.5 percent among those women who did not migrate (Figure 14). A similar pattern is observed among men (Figure 15).

**Women who migrated have, on average, higher levels of educational attainment than those who did not migrate.** Around 22.7 percent of them have upper secondary and 23.6 percent have tertiary education versus 19.5 and 16.6 percent of those who did not migrate, respectively (Figure 16). Comparing across

> 7 Cohabitating refers to individuals who live with their partners in a free relationship without a legal bond.

14
first step is to recognize that the bias of the error is to underestimate the labor force. But then, what can the analyst do after the data have been collected?

Guarcello et al (2010) offer one option. Using data from 24 countries, they constructed a model which would predict LFPR for an individual or group based on characteristics observed consistently in all surveys for that country, as well as dummies for type of questionnaire. They used this model to estimate the LFPR for children aged 10-14 in each country. While their predicted (“harmonized”) LFPR based on individual/household characteristics showed a more consistent pattern than the one derived from the different types of surveys, they noted that a large share of the variation in estimates across surveys is explained by _the type of survey_ , not individual characteristics. They therefore found these results less than satisfactory, since type of survey (CWIQ, DHS, LSMS) is only a weak proxy for the extent of non-sampling error.

> 17 The measurement of earnings encounters many difficulties beyond; recall period is only one of these.

> 18 See annex 1 of World Bank, 2012 which among other things, discusses the analytical implications of the absence of a 12 month recall secondary activity question in the UNHS 2009/10.

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and firms with more capital are less likely to exit. Exporters are less likely to exit.

# **Alternative methods**

There is inherent difficulty of and methodological debates on estimating the production function (Ackerberg et al., 2005; Van Biesebroeck, 2007), which is the crucial element of our empirical procedure. We try several alternative methods of estimation of the production function to reassure the robustness of our results, presented in Table 9. The estimation results of the two-stage Olley-Pakes procedure estimated on the pooled manufacturing sample (columns (1) and (2) of the table) are compared with the estimation results by several other methods. The one-stage Olley-Pakes procedure, analogous to the method presented in panel B of Table 6, but estimated on the pooled manufacturing sample, is presented in column (3). Similarly to the results by manufacturing industries, the one-stage method estimate 31
system.<sup>5</sup> However, we lack data for this variable for many countries that are included in our sample. Instead, we rely on the financial development index of Svirydzenka (2016). This index of financial development distinguishes between financial institutions and financial markets development. For both of these categories, the index further distinguishes between financial depth, access, and effectiveness. We express this conditioning variable as the _ratio of financial markets' depth index to financial institutions' depth index._ Because measures of financial depth might exhibit some volatility over time, this variable enters our regressions as a 3-year average. Fourth, we condition on the share of foreign-owned banks in the domestic banking sector. _Foreign bank ownership_ is expressed as the share of total banking assets held by foreign banks, and the data is taken from the dataset compiled by Panizza (2020).

# **2.2. Stylized facts**

Our data show that average CO2 emissions in kg per PPP $ of GDP for the countries in our sample declined from 0.29 to 0.19 between 1994 and 2019. Over the same period, the average credit-toGDP ratio increased from 33 percent to 57 percent.

Figure A.1 shows the change in CO2 emissions per dollar of GDP and the credit-to-GDP ratio between 1999 and 2019 for 88 out of 125 countries in our sample. Because our panel dataset is unbalanced, we plot the changes for the 20 years between 1999 and 2019 to maximize the number of countries in this figure. Countries fall into one of four categories depending on whether their CO2 emissions per dollar of GDP and credit-to-GDP ratio increased or decreased. Most countries (49) experienced financial deepening coupled with a decrease in CO2 emissions per dollar of GDP over this period. This group includes primarily advanced economies. Meanwhile, 14 countries experienced a decline in CO2 emissions per dollar of GDP and a decrease in financial deepening. At the same time, 22 countries experienced an increase in CO2 emissions per dollar of GDP and
Figure 2: Cumulative distributions of profits and sales at endline <!-- Start of picture text --> 8 us<br>22. 6 ‘<br>f<br>s<br>@5f<br>=<br>®4 f<br>2 f<br>53<br>3° f<br>| —|——— Control group _ --------- Group 1<br><!-- End of picture text -->

<!-- Start of picture text --> bse aera oe -~==-=-- Gowp2 Group 3<br>-200000 oe 200000 400000 600000<br>Profit in the last month (in CFAF)<br>Notes: Data source: Endline surveys 2016, N=2905<br>CDF of sales in the week by group<br>1 en ernie<br>9 er<br><!-- End of picture text --> <!-- Start of picture text --> 8 fe<br>2 ‘<br>Sel |<br>LS>) © f4<br>@ 54 ?<br>2<br>S41 {<br>=<br>E |?<br><!-- End of picture text -->

<!-- Start of picture text --> 24}<br>——— Controlgroup’ --------- Group 1<br>aero COME Group 3<br>0 500000 _ 1000000 1500000<br>Sales in the last week (in CFAF)<br>Notes: Data source: Endline surveys 2016, N=2989<br><!-- End of picture text -->