rafmacalaba/data-use-mentions-extended huggingface dataset:
# 4.4. Who are the women and men who migrated internally?

**This section provides descriptive statistics of women and men who migrated internally and those who did not migrate.** Descriptive statistics are used to create a sociodemographic profile of movers and understand how they differ from those who stayed in the same state between 2015 and 2020. The profiles are constructed using 2020 Census data, meaning they refer to characteristics of women and men five years after the migration decision.

**Women who migrated internally are younger than those who stayed in the same state of residence.** On average, women who migrated are 30 years old, while women who did not migrate are five years older. Moreover, almost 50 percent of migrant women are concentrated in the age group 15-34 years against 34.1 percent of those who did not migrate (Figure 12). This pattern is similar among men (Figure 13).

Figure 12. Distribution of migrant and non-migrant women by age group Figure 13. Distribution of migrant and nonmigrant men by age group <!-- Start of picture text --> Women who migrated Men who migrated<br>Women who did not migrate Men who did not migrate<br>30.0 26.4 30.0 26.2<br>23.2<br>25.0 25.0 21.6<br>20.0 16.8 15.6 20.0 17.2 16.8<br>15.0 15.0<br>8.9 9.7<br>10.0 4.8 4.4 10.0 5.1<br>3.5<br>5.0 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
.059|˗0.121|˗0.115|˗0.046|0.180|˗0.049|
||(0.137)|(0.296)|(0.207)|(0.094)|(0.195)|(0.306)|(0.121)|(0.312)|(0.227)|
|**Constant**|˗4.475|˗0.344|5.208|˗1.827|4.314|3.765|2.582|˗2.894|4.188|
||(3.940)|(8.247)|(5.854)|(2.706)|(5.558)|(8.618)|(3.435)|(8.675)|(6.369)|
|**N (Observations)**|470|249|236|526|298|273|465|214|208|

_Source:_ Authors’ calculations based on ONDH impact evaluation panel survey. _Note:_ *p < 0.1; **p < 0.05; ***p < 0.01. Standard errors clustered on commune level.

# 5.5.2 RDD Results

Turning now to the RDD results on ECD, the estimates for each year and across different bandwidths are presented in Table 9 and the RDD graphs in Figure 6 and Figure 7. As with the economic outcomes, the figures show results with a bandwidth of 50 percent. Other bandwidths have relatively smoother results. As with economic outcomes, there are substantial fluctuations over the range of poverty rates with differences at the discontinuity being largely comparable to fluctuations. Results are almost all insignificant, a mix of negative and positive estimates that vary substantially by bandwidth. While in 2011 there are positive prenatal and delivery effects that
mechanism, we examine if the effects of robotization on imports are heterogeneous across intermediate and other goods. Although it is empirically challenging to precisely identify imports of intermediate goods (and distinguish them from imports of other goods), we use two product-level classifications that have been widely adopted in the literature to achieve this objective. First, the BACI data set makes it possible to distinguish between intermediate inputs from other goods using the Broad Economic Categories Classification of the UN (see Appendix B.1 for details). Second, we adopt the approach proposed by Schott (2004), who identifies as inputs only those goods for which the product codes explicitly contain the words “parts” or “components”. For both these classifications, the IV estimates in columns (2) and (4) in Table 7 reveal that robotization has a considerably stronger positive impact on imports of intermediate than on other goods.

# **6 Counterfactual simulations**

In this section, we use our theory to perform counterfactual simulations on the general equilibrium effects of reductions in robot prices on trade patterns, wages and welfare in the North and the South. To approximate the setting we explored in the regressions, we calibrate a model with three countries and three sectors. We consider a representative high-income Northern country, a representative country in the South, and a group of other (lower-income) developed countries. The existence of a group of other developed countries allows us to examine the extent to which the quantified model is able to reproduce the relative trade effects we documented in the regressions.<sup>13</sup> Among the three sectors considered, two sectors are tradable and the other sector is nontradable. Production may be subject to robotization only in one of the tradable sectors. This sector consists of automotive, rubber and plastic, electronics, chemicals, metal and machinery industries. The non-robotized tradable sector consists of all other manufacturing industries, including food and textiles, agriculture, mining and utilities. The non-tradable sector consists of construction and services
, the nature and magnitude of the relation between growth and savings will be tested using Egypt’s data and provide an explanation to the ongoing debate.

# **Financial Development**

The earlier work of McKinnon (1973) and Shaw (1973) as well as the large body of contributions that followed assumed that financial development was indicative to the increased saving rate. Financial development policies called for elimination of credit ceilings, interest rate liberalization, ease of capital flows, enhanced prudential guidelines and supervision, and the development of capital markets.

Loayza and Shankar (2000) empirically show that – from a portfolio composition perspective - financial development allowed the private sector to increase long-term assets and durable goods within their portfolios of financial assets.

Loayza et al. (2000) propose that financial development has a direct short-run impact on saving rates and an indirect positive long-run impact. The direct effect goes through price and quantity channels. The price channel works through higher interest rates and, although popularly advocated in operational documents and the financial press, it is rarely effective in raising private saving— suggesting that the negative income effect of higher interest rates tends to neutralize their positive intertemporal substitution effect. Expanding access to credit to previously credit-constrained private agents allows households and small firms to use collateral more widely, and thus reduce 13
Policy Research Working Paper 7935

# **Abstract**

Global poverty is becoming increasingly concentrated in Sub-Saharan Africa and among households engaged in subsistence agriculture in environments characterized by uncertainty. Understanding how to achieve sustainable increases in household incomes in this context is key to ending extreme poverty. Uganda offers important lessons in this regard. Uganda experienced conflict, drought, and price volatility in the decade from 2003 to 2013, while at the same time experiencing the second fastest percentage point reduction in extreme poverty per year in Sub-Saharan Africa. This study analyzes a nationally representative panel of 2,356 households visited four times between 2006 to 2012, in combination with data on conflict events, weather, and prices. The study describes the type of income growth households experienced and assesses the importance of these external events in determining progress. The study finds substantial growth in agricultural incomes, particularly among poorer households. Many of the gains in agricultural income growth came about because of good weather, peace, and prices, and not technological change or profound changes in agricultural production. Therefore, although overall progress during this period was good, there were years in which average income growth was negative. This was particularly the case in the poorer and more vulnerable Northern and Eastern regions, and thus their overall income growth was also slower.

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

_The Policy Research Working Paper Series disseminates the findings of work in progress to encourage the exchange of ideas about development issues. An objective of the series is to get the findings out quickly, even if the presentations are less than fully polished. The papers carry the names of the authors and should be cited accordingly. The
**Table A5: Country-Specific Proportion of Sibling Correlations Explained by Intergenerational Transmission Estimates (1970 Cohort)**

|**Country**|**Prop.**|**S.E.**|**N**|**Country**|**Prop.**|**S.E.**|**N**|
|---|---|---|---|---|---|---|---|
|East Asia & Pacific||||Sub-Saharan Africa||||
|Cambodia|0.731|0.024|2271|Benin|0.867|0.037|1028|
|Indonesia|0.787|0.009|12924|Burkina Faso|0.844|0.043|1314|
|Philippines|0.816|0.021|4435|Burundi|n.a|n.a|n.a|
|Vietnam|0.829|0.022|2286|Cameroon|0.742|0.073|399|
|||||Chad|0.735|0.039|658|
|Europe & Central Asia||||Congo, Rep.|0.926|0.134|275|
|Albania|n.a|n.a|n.a|Cote d'Ivoire|0.737|0.050|507|
|Armenia|0.940|0.076|1011|Ethiopia|0.749|0.017|2106|
|Kyrgyz Republic|0.496|0.112|468|Gabon|0.890|0.096|478|
|Tajikistan|n.a|n.a|n.a|Ghana|0.853|0.046|980|
|Turkey|0.819
## <u>Inputs:</u>

1. Household Survey with consumption or income welfare aggregates

2. Project data/Other survey data without welfare aggregates 3. Set of harmonized common variables in both surveys

## <u>Outputs:</u>

1. Set of imputed welfare variables for project data/other survey for each household in the data

2. Imputed welfare variables can be used for poverty, distributional analysis (quintiles or more), profiling of the poor or group of interest

## <u>Models:</u>

1. Ordinary Least Squares (OLS)

2. Probit 3. Multiple Imputation (MI) Table 18. Model Specification

||**Variables**|
|---|---|
|**Demographic**|Share of children,share of adults,share of adults squared and share of old(omitted)|
|**Characteristics of head**|Age, gender,and level of education|
|**Interactions with urban dummy variable**|Level of education of the head,age of the head|
|**Geography**|Dummies for regions at NUTS 1 level(12 regions)|
|**Interactions with Geography**|Level of education of the head, age of the head interacted with regions at NUTS 1<br>level(12 regions)and urban-rural division|

# **1. Validation and Robustness Check**

Figure 7. External Validation, NUTS1 Level $5/day PPP– Observed (SILC) & Imputed (LFS), 2007 <!-- Start of picture text --> LFS<br>SILC<br><!-- End of picture text --> 35
834|
||(1.198)|(1.914)|(1.912)|
|Dummy_Mais Educação_* year 2009|-0.313|-5.514***|-2.792*|
||(0.970)|(1.664)|(1.674)|
|Dummy_Mais Educação_* GDP per capita|0.169|0.201|0.423|
||(0.151)|(0.285)|(0.337)|
|_R_ <sup>2</sup>|0.736|0.755|0.742|
|Observations|2,595|2,595|2,595|
|Dummy year 2009|Yes|Yes|Yes|
|Dummy year 2011|Yes|Yes|Yes|
|School fixed effects|Yes|Yes|Yes|

_Sources:_ Authors’ calculations using MEC/FNDE administrative data, _Prova Brasil,_ School Census, and INEP.

_Note:_ Treatment group are schools that joined _Mais Educação_ in 2008 and remained in the program until 2011. GDP per capita was deflated by IPCA, and is in 2007 prices. This table reports coefficients from the ordinary least squares estimation of equation (6) in the paper. The dependent variable is in column (1) is dropout rates, in column (2) mathematics grades in _Prova Brasil,_ in column (3) Portuguese grades in _Prova Brasil._ All variables are at school level. ***significance at the 1% level; **significance at the 5% level; *significance at the 10% level. Robust standard errors, clustered at school level, are reported in parentheses. All regressions include school fixed effects and time dummies. All regressions are weighted by propensity score matching weights. Panel A reports these estimates for the schools from the fifth-grade sample. Panel B reports these estimates for schools from the ninth-grade
the Caribbean. Taken together, these regions account for 78 percent of observations and if not corrected, results will be dominated by these two regions. To correct the bias towards countries with many surveys, we construct weights equal to the inverse number of spells in each country.<sup>6</sup> By doing this, we made sure each country gets an equal weight of one in the analysis regardless of the number of spells available. This changes the regional distribution of spells by giving more weight to countries with fewer observations. The share of the Europe and Central Asia (ECA) and Latin America and the Caribbean (LAC) regions drops from 78 percent to 50 percent once weights are used, while the share of spells in Sub-Saharan Africa (SSA) increases from 5 to 24 percent. In terms of distribution through time, the lion’s share of surveys started after 2010 (almost half of all weighted observations), followed by the period from 2000 to 2010 (37 percent) and the period before 2000 (14 percent). This is in line with the substantial investments in household survey data collection in low-income countries, and SSA in particular, since 2010.

For each spell, we calculate mean household consumption (or income) per capita (from surveys) as well as the US$2.15 a day poverty rate at both the start and the end of the spell. Survey means are expressed in 2017 Purchasing Power Parity (PPP) terms for reasons of comparability. Each of the country-spell years is merged with national accounts data (GDP and household final consumption expenditures, expressed in constant dollars and per capita terms) from the World Development Indicators (WDI).<sup>7</sup> Next, we calculate the “descriptive elasticity”: The annualized change in poverty during a spell divided by the annualized change in survey mean consumption/income per capita during the spell (alternatively: GDP per capita). This results in an estimate of the GEP:
economy as a benchmark, given that the United States has a relatively low intensity of bribing.<sup>16</sup> Third, to evaluate the quantitative effects of rent-seeking in the form of bribes, I calibrated the rest of the parameters to match relevant features of bribing in Argentina. All data moments and targets are based on the WBES, unless otherwise stated. One year in the model is equivalent to one year in the data.

# **4.1. Moment Selection and Calibration**

**Standard parameters** . Values for the model’s standard parameters can be found in table 2. The discount factor _β_ is calibrated to match a real interest rate _r_ of 4 percent. This rate implies a calibrated impatience rate of 0.05. The depreciation rate Ω is set to imply an average investment to capital ratio of approximately 6 percent, which corresponds to the average value for the private capital stock in the United States. Following the literature, I set the share of capital in value added at _α_ = 1 ⁄ 3. Finally, I set the elasticity of substitution at _ρ_ = 3, as in Hsieh and Klenow (2014).<sup>17</sup>

**Table 2. Standard parameters**

|**Parameter**|**Value**|**Target/source**|
|---|---|---|
|Share of income to capital|_α_= 0.33|Literature|
|Elasticity of substitution|_ρ_= 3|Broda and Weinstein (2006)|
|Depreciation rate|Ω = 0.06|Literature|
|Discount factor|_β_= 0.96|Interest rate of 4%|

**Technological and innovation parameters** . Six parameters need to be jointly calibrated to match six relevant moments for the U.S. economy. For technical details of the computational procedure used to calibrate them, see appendix B.4.
Specifically, we used an adapted version of the Ages-and-Stages Questionnaire (ASQ) (Bricker et al., 1999) to collect child development measures relating to communication, gross and fine motor coordination, problem solving, and personal-social development.<sup>10</sup> The ASQ was primarily administered by enumerators specially trained for this activity, with the exception of items relating to child behaviors, which are typically difficult to observe during a household visit. These items were reported by the primary caregiver of the target child. We report an aggregated ASQ score and scores for each of the domains. Scores are standardized using the means and standard deviations of the control group.

At endline, we measured child development using the direct assessment tool of the Measurement of Development Early Learning (MODEL) to asses the school readiness level of target children in our sample.<sup>11</sup> We collected information to measure domains related to early literacy and math skills, executive function (working memory), socialemotional motor skills for and interest in This as- development, fine writing, literacy. sessment was performed at home in the presence of the primary caregiver. We estimate scores separately for each of these domains. Scores are standardized using the means and standard deviations of the control group.

# **Outcomes on other household members**

_Parental Practices._ To measure the of we used an ver- quality parental practices, adapted sion of the UNICEF Multi-Indicator Cluster Surveys (MICS) and its Family Care Indicator (Hamadani et al., 2010; Kariger et al., 2012) to collect data on (i) the provision of stimulation for children and (ii) the home learning environment (home play activities). The two outcomes were collected at follow up, and we also collected data on the stimulation provided by any member of the household at the beginning of the study. We estimate

> 10The ASQ is composed of age-specific sections, dividing children aged 36 to 59 months into 4 groups of 6-month
# **[Insert Table 3]**

Second, the number of people to be found in the IRS households and the non-IRS households in the three malaria endemic categories was estimated according to the following assumption:

|Households in_Low_Endemicity Category:|10% IRS|90% non-IRS|
|---|---|---|
|Households in_Medium_Endemicity Category:|50% IRS|50% non-IRS|
|Households in_High_Endemicity Category:|80% IRS|20% non-IRS|

Finally, in order to determine the number of IRS spray operators in a given area, data from IRS training manuals that indicate that 50,000 structures need 175 spray operators were used (USAID, 2009). The number of spray operators in each country was then calculated based on the number of IRS households.

The country-level estimates of the IRS households, the non-IRS households and the spray operators were later combined with the respective RR in each of the three population groups to estimate the increased incidence of the unintended diseases (health externalities) due to the DDT exposure.

# **_4. Health Externalities of DDT_**

The direct and indirect economic losses arising from the increased risk of unintended diseases from the exposure to DDT in the affected population were then separately computed. The direct economic loss results from the treatment costs needed to treat the diseases, while the indirect economic costs arise as a consequence of increased morbidity and mortality.

The cost estimates due to DDT exposure were done in this paper under two alternative extreme scenarios: _DDT use widespread_ and _DDT use restricted_ . In the _DDT use widespread_ scenario, it was assumed that all three malaria endemicity areas: _low_ , _medium_ and _high_ will be subject to IRS with DDT of varying magnitudes; and therefore all the 71 countries with malaria endemicity 10
(0.015)|(0.013)|(0.016)|(0.016)|
|Observations|3,596|3,061|2,929|3,061|2,929|
|R‐squared|0.392|0.436|0.453|0.446|0.464|
|Adjusted R‐squared|0.086|0.072|0.075|0.090|0.094|
|Mean dependent variable in Control|0.023|0.052|0.026|0.059|0.040|
|Pvalue Test Group1=Group2|0.175|0.153|0.353|0.257|0.602|
|Pvalue Test Group1=Group3|0.003|0.017|0.028|0.015|0.075|
|Pvalue Test Group2=Group3|0.022|0.211|0.066|0.068|0.057|
|Pvalue Test Group1=Group2=Group3|0.002|0.037|0.026|0.016|0.049|
|Pvalue Test Group1=Group2=Group3=0<br>Note: Administrative data from GUFE and<br>variable on treatment dummies, controll<br>* indicate statistical significance at 1, 5|0.000<br>survey da<br>ing for str<br>and 10%.|0.000<br>ta May 201<br>ata dummie|0.000<br>6. OLS regres<br>s (dummies|0.000<br>sion of the o<br>for each tri|0.000<br>utcome<br>plet). ***, **,|

35
>Benchmark sign restr. Benchmark sign restr. Benchmark sign restr.<br>Add.<br>Add. Add. Add.<br>Benchmark sign restr. Benchmark sign restr. Benchmark sign restr.<br>Add. Add. Add.<br>Benchmark sign restr. Benchmark sign restr. Benchmark sign restr.<br>sign restr.<br>Add. Add. Add.<br>Benchmark sign restr. Benchmark sign restr. Benchmark<br><!-- End of picture text --> Note: Pass-throughs are defined as the ratio between the one-year cumulative impulse response of consumer price inflation and the one-year cumulative impulse response of the exchange rate change to shocks from country-specific factor-augmented vector autoregression models estimated for 51 economies (29 advanced economies and 22 EMDEs) over 1998-2017. A positive pass-through means that a currency depreciation is associated with higher inflation. Bars show the interquartile range and markers represent the median across countries. In the alternative specification, an additional sign restriction was imposed, assuming that a positive domestic demand shock leads to a contemporaneous increase in domestic interest rates. EMDEs = emerging market and developing economies.

40
The connection between Indonesia and other countries through GVCs may affect the exclusion restriction of our instrumental variable. For instance, global demand for Chinese products can impact Indonesian skills demand through the inputs Indonesia provides to China. We check for this possibility by excluding from the data set the mining and chemical industries, which are strongly involved in GVCs by downstream links –intermediate products Indonesia provides for other countries’ exports (OECD, 2013).

We present the main results for the 2005-2010 sub-period in Tables 26 and 27 for the skills demand model and aggregate labor market indicators models respectively.<sup>7</sup> Results confirm previous findings.

Results for the 2011-2015 sub-period show some differences with respect to previous findings (Tables 28 and 29). We cannot confirm the reduction in the demand for analytical and interpersonal skills and the increase of non-routine manual skills associated with the technology variable. Our previous explanation for this finding was that our proxy for the technological development available for firms in each provincial labor market was capturing a more advanced type of technology and not just the adoption of computers during 2011-2015. The lack of this result when excluding industries more strongly involved in GVCs would indicate that the adoption of more advanced types of technologies took place in these industries. Also contrary to earlier results, we do not find that an increase in employment is associated with an increase in exports and we do find that an increase in technology adoption is associated with a reduction in labor force participation and employment. We argue that industries involved in GVCs explained the previous increase in employment linked to exports and the lack of effect of the technology measure.

# _8.2 Changing the Source of Data for the Skills Measure_

Results on the skills measures presented in the previous section were based on the Philippines STEP Survey. In order to check the sensitivity of our results to the use of a different data source, we follow previous literature in defining skills measures using the U.S. O*Net database. We rely on 2004 O*Net data
very similar to those for emerging market firms (Appendix Figure 2). These two sets of firms share two important characteristics. First, they are inherently riskier than investment grade developed market firms. Furthermore, these high-yield developed economy firms also can be included in special indexes that are similar to the CEMBI and EMBI. The Bloomberg Barclays High Yield Very Liquid Index is an important benchmark for these firms that only includes high-yield dollar-denominated debt from developed market firms, with a minimum issue size of $500 million.

> 19 <mark>Appendix Table 3 reports similar results using narrower bins.</mark>

19