rafmacalaba/data-use-mentions-extended huggingface dataset:
We cannot rule out that the difference in caregiver-reported diarrhea prevalence between groups could be an artifact of biased outcome reporting. However, we do not observe differences between groups in falsification symptoms that could be subject to similar bias. Moreover, the results from the fecal samples (arguably more objective data than caregiver-reported diarrhea prevalence) show benefits accruing even more narrowly to non-poor households that had no sanitation at baseline, which are the group most likely to have built toilets as a result of the program. The weight and height improvements are also restricted to this group.

Positive externalities are another potential explanation for the large estimated impact on diarrhea prevalence. Perhaps only small improvements in sanitation are needed to give rise to large health effects given the benefits that flow across the community as a result of a less contaminated environment. The benefits of a household’s new toilet extend beyond that household’s. This is an area we have not fully explored in this report but is an area for future research.

# **10. CONCLUSION**

In conclusion, knowledge of the link between poor sanitation and health seems to be high in East Java even in control communities. Nevertheless, open defecation is still tolerated and many households believe that other people defecating in the open has no bearing on their own household’s health. Defecating in rivers is still a relatively attractive option for many households. A qualitative study conducted by WSP (2012) found that communities located near rivers are less likely to achieve ODF status. Households in these areas considered defecating in the river superior to using a pit latrine. This suggests that specific strategies focused directly at communities that are in close proximity to water bodies might be needed.

Nevertheless, we find that TSSM is associated with sanitation improvements. More households built toilets in treatment communities (3 percentage points higher) than control communities. This is a 31 percent increase in the rate of toilet construction in treatment communities relative to control communities. Further, fewer households reported that household members ever defecate in the open
- 6 -

# **II. EMPIRICAL MODELS AND DATA**

The new RAI is defined by the proportion of the rural population who live within 2 km of the nearest road in good condition. Road conditions are assessed in different ways, depending on the available data in the countries. In general, the assessment is conducted based on the surface type and roughness of each road. From an engineering point of view, the RAI only takes into account paved roads in good and fair condition and unpaved roads in good condition (see World Bank (2016) for further details).

Urban areas are excluded using the Global Rural-Urban Mapping Project (GRUMP) data set. The population distribution data come from WorldPop, which is available at a resolution of 100 meters and regularly updated whenever new data (such as national census data) become available.

World Bank (2016) shows that the new RAI estimates are somewhat different from the original estimates in 2006, mainly because of methodological differences. Among the sample countries where results are available, rural access varies significantly, from 17 percent in Zambia to 56 percent in Kenya and 87 percent in Bangladesh (table 1). An advantage of the new RAI method is that the index is available not only at the national level, but also at the subnational level. The latter is the primary data source for the following empirical analysis (map 2).

**<u>Table 1. Summary of the New RAI Estimates</u>**

|Country|Population<br>(million)|Land area<br>(1,000 km<sup>2</sup>)|Population<br>density|<br>Road data<br>length(km)|Original<br>RAI, 2006|New<br>RAI|Year|
|---|---|---|---|---|---|---|---
household ownership of DVD, television, MP3 player, cellular, phone, computer, and calculator.

Table 7c: Impacts of PROSPERA on Type of Secondary School: Sample: 6<sup>th</sup> graders in 2008 in Medium, High and Very Highly Marginalized Communities Nearest Neighbor Matching Models

|Variables|7th grade<br>Telesecondary|Enrollment<br>7th grade General<br>school|7th grade Technical<br>school|
|---|---|---|---|
||Propensity score matching|with exact matching on age an|d state|
|Girls|0.1263***|-0.0209|-0.0912***|
||(0.0164)|(0.0149)|(0.0188)|
|Observations|6,654|6,654|6,654|
|Boys|0.104***|-0.014|-0.0920***|
||(0.0212)|(0.0133)|(0.022)|
|Observations|4,853|4,853|4,853|

Note: Estimates significance level *** p<0.01, ** p<0.05, * p<0.1. We report standard errors in parenthesis. Variables which enter the propensity score include: age, number of siblings, household size, lives with mother, lives with father, health problems, whether currently working, household ownership of DVD, television, MP3 player, cellular, phone, computer, calculator, student teacher ratio, whether the school has a computer, whether the school has rooms with more than one grade, average teacher education levels and marginality index components. Community level variables include the margination index, proportion illiterate, proportion of households without piped water, proportion without electricity and proportion without drainage.

Why is the increase
United Kingdom, France, Ireland and Estonia, where transfers make up to 14% of gross income. Over time, however, there have been slight differences across countries. In the case of Germany and Poland, policy changes to the transfer system increased the amount of transfers to workers in non-routine, manual task intensive occupations by close to one percentage point of their gross income. In the case of Spain, policy changes played no role in the observed increase in the share of transfer over gross income across all occupational groups. Instead, the entire change can be explained by the automatic reaction of the system in a context of changing market income.

**Figure 8. Limited role of policy changes in the transfer system across occupations: Average incidence of transfers, 2007-2014**

_Source:_ Authors’ calculations based on EUROMOD H1.0+.

_Note:_ Occupational groups are the following: non-routine, cognitive task intensive occupations (ISCO 08 major groups 1, 2 3); routine task intensive occupations (ISCO 08 major groups 4, 7 8); non-routine, manual task intensive occupations (ISCO 08 major groups 5, 6, 9). The light blue bar indicates the average tax and social contributions rate, calculated as a share of gross income, for 2007. The navy color bar indicates the same variable for 2014. The blue bar indicates the counterfactual rate in 2014 had the tax and transfer system been the same as that of 2007.

Most of the remaining countries in Europe have shown small, but still positive, policy-driven increases in the transfers for workers in routine task intensive and non-routine, manual task intensive occupations. In Cyprus, for instance, workers in non-routine, manual task intensive jobs experienced a transfer increase from 8% to 14% of their gross income between 2007 and 2014. Of 20
and investment). A series of papers by Fan (see Table 2) use time-series data to estimate the relationship between public expenditure components in rural areas on both rural income growth and poverty reduction. The results suggest that there are large returns to agricultural research with respect to income growth and poverty reduction, though there are significant returns from other types of public spending as well. They examine spatial variations in income growth and poverty reduction and find that spending has the larger effects in poorer regions.

- _Rates of return studies._ Some studies use data drawn from household surveys to test the impact of education on individual earnings (rate of return to education) or aggregate data on public investment, output, and employment to estimate the returns to government investment. There is a large literature on rates of return to education investments (see for example Psacharopolous 1995), agricultural research (Roseboom 2002), infrastructure investments (Canning and Bennathan 2000), and in some cases, health investments (Schultz 1997 and 2002). However, the calculation of rates of return has typically been conducted to compare spending allocation _within_ sectors. To the best of our knowledge, there has not been an attempt to compare these rates of return across sectors within a consistent empirical framework, due to the many technical difficulties inherent in such an exercise.

**II. General equilibrium models** _._ Computable General Equilibrium (CGE) models form a class of models where production activities, factors, and institutions and their links are fully specified. These require both national accounts and survey data. The data are compiled into a single information matrix (social accounting matrix (SAM)), where the links between activities, factors, and institutions are organized and calibrated using countryspecific parameters. Because they are technically demanding and data-intensive, they have been rarely used.

- Jung and Thorbecke (2003) apply and calibrate CGE models for Tanzania and Zambia. Their simulations suggest that a well-targeted education expenditure can be effective for growth and poverty alleviation.
, a set of time-varying household control variables. These include: household size; access to electricity; whether the household dwelling is ii ii ii owned; and an index measuring dwelling characteristics.αα γγ XX<sup>18</sup> This is broadly in line with the controls used in the literature.<sup>19</sup> Further, we add access to 2G coverage to ensure that the analysis isolates the impact of upgrading coverage to 3G/4G and does not conflate it with the impact of getting 2G coverage, which can be used for voice, SMS and limited internet access (all of which could impact our outcome

> 15 More specifically, we rely on GPS coordinates of households with the maximum offset of 45 meters to assign to each household the initial date of coverage (if any) by any combination of radio bearer and signal strength. In contrast, other studies have relied on data integration using buffer sizes between 1 to 5 kms in urban areas, and 1 to 20 kms in rural areas (see for instance DHS -- Demographic and Health Surveys).

> 16 In addition to identifying time dynamics with dummy variables, we also measured internet access as the cumulative number of months since a given household was first covered by 3G/4G services (instead of a binary approach). The results of this analysis are presented in Table A 1 in Appendix 1.

> 17 We also carry out a sensitivity analysis and estimate the model in first-differences. The results are presented in Table A 2 and are roughly similar to our fixed effects estimates. Given that we account for serial correlation in the fixed effects regression by clustering standard errors by household, the latter is our preferred strategy because it maximizes the number of observations, as not all households are included across all three waves of the survey. Moreover, any bias due to noncontemporaneous correlation between the error term and the independent variables is lower with fixed effects.

> 18 The index is based on whether the household has the following
|1.1|1|2.4|0.8|
|Other emergingec.|0.2|0.2|0.4|0.1|0.7|0.6|1.9|0.3|

Notes: shares of nonoil exports. *"Other" policy-imposing economies include Pakistan, Peru, Thailand, Colombia, Malaysia, Philippines, Chile, New Zealand, Israel, and Taiwan, China. All includes the same list of countries as in Table 6.

Source: Bown (2012).

Data on antidumping investigations show that EAP countries have not opened antidumping investigations against Eurasian economies. But this is not the case with the EU, as most of the countries of the region have been exposed to antidumping investigations. Russia is the country with the highest number of investigations opened by the EU, 32, followed by Ukraine, 21, and Belarus, 9. In the case of Russia, 84.3% of the cases were associated to a final affirmative dumping decision, while for Ukraine it was 76.2%. For the rest of the economies, the rate of affirmative cases varied between 0% for Uzbekistan, Turkmenistan, and Georgia, and 100% for Moldova and Kyrgyzstan. Safeguards in the EU are related to products such as steel, mandarins, salmon, strawberries, and wireless wide area 16
the immediate past. All of these sources of error can be considered non-deliberative or unintentional. On the other hand, intentional misreporting can arise if there is perceived social pressure to appear either “wealthier” or “poorer” to the interviewer and thus the social context of the interview may also be relevant. Finally the interviewer and respondent could suffer from fatigue in the latter part of the interview when surveys are long, or lack integrity when supervision is limited.

The evidence on the implications of consumption survey design which we review is fragmentary and typically sheds light on only one aspect of survey design while ignoring resource implications of the variants considered. Thus previous work at best either explicitly or implicitly investigates how only one source of reporting error varies with design. For example, systematic variation in the length of recall can explore the net relative effect of recall and telescoping errors at different reference periods. A challenge to this previous research is the lack of a consumption benchmark, making it difficult to conclude one design is more accurate than another since there are no data on actual consumption to validate the survey estimates. Scanner data may allow validation in certain contexts in developed countries but will be unavailable for many years in developing countries where many goods are either home produced or bought in informal markets. Prior attempts to form a benchmark for consumption surveys in developing countries have not been fully successful. For example, in India’s NSS experiments enumerators visited households every day and gave volumetric containers for measuring food consumption but for some foods less than two-thirds of respondents used the containers and many respondents did not use the daily diary given to them (NSSO 2003).<sup>6</sup>

# _Diary or Recall_

In a non-negligible number of developing countries, including Brazil, China, and many countries of Central Europe and Central Asia (where literacy rates are high), there exists a tradition of diary-based collection of consumption data – at least for certain consumption items such as food. This contrasts with the more common practice in Living Standards
agricultural PAC money were members of the influential House Agricultural committee. They included the committee chair (de la Garza ) and three subcommittee chairs (Stenholm, Rose, Volkmer). Figure 2 bears out much the same story for the 1997-98 election cycle. The pattern of giving in these figures clearly indicates that agricultural PACs targeted politicians with influence over agriculture policy. The amounts are not inconsequential. For example, in the 1993-94 cycle, agricultural PACs delivered between 8% (Fazio) and 60% (de la Garza) of the total PAC money received by the top twenty “Ag PAC” recipients.<sup>41</sup>

# _Instrumental Variables_

Since the regressor is composed of the endogenous variables _I_ and _z_ we construct two exogenous variables from the agriculture census of 1997 that identify each of the endogenous variables. The first instrument, termed PaymentConcentration, is a measure of geographic concentration and pressure. It is constructed using data on total government payments available for each state. Total government payment to a state is then distributed across the top four commodities produced in that state.<sup>42</sup> This approximates how much each commodity gets by virtue of its being a visible top four commodity in the state. Summing across the states gives us variable PaymentConcentration which measures government payments to each visible commodity. The second instrumental variable termed Farmland is simply the acres of land in farms for each crop. The first variable is constructed in order to identify political organization and the second to identify the output-to-imports and output-to-exports ratio _z_ . Since the regressor in model (4) is a composite of the two, for identification we use linear, quadratic, and cross-product of these two variables as our set of instruments.

files, and then merged with the relevant cycle’s CN files. Agriculture committee and sub-committee assignments for each Congress were obtained from Congressional Quarterly (1996-1999).

41 Top 20 list-makers who were not on the Agricultural committee represented districts with influential agricultural constituents (CA, OK, VA, MI
After selecting variables by applying Lasso to a single imputation, we estimate the post-lasso OLS regressions across all 100 imputations, take the average of the point estimates, and estimate the variance using Rubin’s rule, as is standard when using multiple imputations.<sup>11</sup> To examine the predictions in the first wave, we estimate the following equation in the HFPS wave 1 data: wave 1 data: 0 1R , where indicates the subnational region. ii and R ii denote the lasso-selected set of HFPS cc WW = αα + αα RR + εε well-being and real-time indicators, respectively. ii is a random disturbance term, clustered on ii ii WW RR country. cc εε For the long-term analysis, we consider the following OLS model using the demeaned variables, 0 1R� , where and R� denote the set of demeaned HFPS well-being and real-time indicators iiww iiww ccww WW<sup>�</sup> = ββ + ββ RR + εε respectively for wave w in region i. iiww iiww is a random disturbance term, clustered on countryWW<sup>�</sup> RR wave. Using the demeaned variables is equivalent, at least in terms of the coefficient estimates, ccww εε as a model in levels that includes regional fixed effects in the estimation. The resulting R<sup>2</sup> measures provide a measure of how well the selected real-time indicators explain the variation in outcomes, within regions across time.

# **3. Trend Analysis**

# 3.1 Trend of HFPS Well-Being Measures

Figure 1 presents the trend of five well-being measures in 2020, averaging over countries in the sample. Not all measures are available for each country in each wave, so the composition of countries varies by measure and month. In addition, the first wave of the survey asks about changes since the pandemic began, while subsequent months ask about changes since the previous survey wave for that country. As the surveys start in April, we cannot report changes
# **3 Data and Empirical Strategy**

## **3.1 Data**

To estimate the innovation premium in Brazil, we use the RAIS database (Rela ̧c ̃ao Anual de Informa ̧c ̃oes Sociais) from 2014 to 2017. This is an administrative database from the Brazilian Ministry of Economy considered a high-quality census of the Brazilian formal labor market. The census includes all establishments nationwide with at least one registered worker. We use data on over 40 million employees per year, matched with firm and establishment information, including location and industry. At the individual level, the database includes information on workers’ gender, age, education, employment status, type of contract, tenure, data of hiring, among others. We excluded observations where the data were clearly miscoded or missing, as well as individuals younger than 18 or older than 65 years old. We also restricted our sample to one observation per worker-year. We impose this restriction by choosing the highest-paying in any given year. Finally, we have opted for using data at the level to account for characteristics.

Due to the large size of the database, we focus on the period 2014 to 2017. This period represents a period of stagnation in the economy with recessions in 2015 and 2016 when GDP growth dropped 3.5% and 3.2%, respectively. Thus, this is a period of stagnation of wages, and it is possible that since the wage premium oscillates with the business cycle, the estimates for the period provide a lower bound of the premium.

## **3.2 Classification of occupations**

The RAIS database also includes information on the occupation of each worker, coded according to the Brazilian Code of Occupations (CBO). To improve comparability, we matched CBO with two other codes, the International Standard Classification of Occupations – ISCO08 and a match between the National Qualification Framework (NQF) from the United Kingdom and the Standard Occupation Code (SOC). These two codes
<br>1<br>0.8<br>0.6<br>0.4<br>BUX MAX VP(2-8)<br>Dec-97 Mar-98 Jun-98 Sep-98 Dec-98 Mar-99 Jun-99 Sep-99 Dec-99 Mar-00 Jun-00 Sep-00 Dec-00 Mar-01 Jun-01 Sep-01 Dec-01 Mar-02 Jun-02 Sep-02 Dec-02 Mar-03 Jun-03 Sep-03 Dec-03 Mar-04 Jun-04 Sep-04<br><!-- End of picture text --> Source: AKK and MNB data.

29. Thirdly, there is a prevalent view among market participants and regulators that pension fund portfolios in Hungary are conservative because most asset management companies are subsidiaries of mother companies in Continental Europe, i.e., from countries that do not have an “equity culture”. According to this view, the presence of a larger number of subsidiaries of Anglo-Saxon companies in Poland would also explain the larger holdings of equity in the Polish second pillar, relatively to Hungary’s. Finally, members of the few voluntary pension funds that provide portfolio choices have favored more conservative portfolios, made up almost exclusively of Government bonds (OECD 2005). Although the experience with multiple portfolios is very recent and limited (only 5
as they aimed to meet operating targets for the money supply). This volatility is an important consideration in choosing the appropriate criterion for the identification of the turning points in rate cycles.

Next, we augment this monthly data on policy interest rates with dummy variables for major QE or QT programs. Incorporating these programs is important to capture changes in the evolution of monetary policy in several parts of the subsequent analysis, particularly when policy rates in several economies were at the lower bound and central banks relied on asset purchase programs to adjust policy. These dummy variables equal one on the announcement date of a new program (even if the program is not implemented until later) and revert to zero when the program ends.<sup>11</sup> These QE and QT dummies only include programs related to monetary policy (including balance sheet run-off through passive QT), but do not include programs primarily aimed at providing liquidity or addressing market dysfunction. The data used to compile these balance sheet variables are: CGFS (2019) and BIS (2019) for programs before the pandemic, English et al. (2021) and Fratto et al. (2021) for QE programs in response to the pandemic, and Du, Forbes, and Luzzetti (2024) and English et al. (2024) for QT programs since the pandemic.

# II.3. The Dates of the Rate Cycles

Application of our algorithm to the data on nominal policy rates and balance sheet programs yields 212 distinct rate phases (111 of tightening and 101 of easing) from January 1970 through May 2024 for our sample of 24 advanced economies. This includes the phase that each economy is in as of May 2024—even if that phase is not completed.<sup>12</sup> Appendix Table 1 lists the identified turning points at the start of tightening and easing phases. The near symmetry of the blocks for each country—with each tightening phase followed by an easing phase (and vice versa) and a roughly equal