rafmacalaba/data-use-mentions-extended huggingface dataset:
chosen level of aggregation.<sup>28</sup> The final expression below accounts for the _ex-post_ adjusted sampling weights:

> 27 Since the probability of being selected in each stage is a function of the cluster size and the number of units sampled in each stage, the overall probability of being selected in most living standards household surveys is constant at the level in which PSUs are allocated (i.e. sub-stratum in the case of the HIES 2016/17). This is because PSUs are generally allocated across stratum using proportional allocation based on population size or number of households, which results in a constant probability in the first stage as long as no adjustments are made, such as post-stratification or update of the population based on the listing exercise.

> 28 Depending on the sampling frame used for the sampling design, and whether a listing exercise or poststratification actually takes place or not, the sum of the household-level probability weights in the survey will

33
innovation is self‐reported and becomes a highly subjective concept, particularly when innovation refers to “significant” improvements. Therefore, the distinction between innovations and mere product, process, marketing or organizational changes becomes murky.

In recent years, several authors have advocated for a focus on knowledge capital assets rather than on innovation inputs or outcomes, leading to a renewed effort to capture investments in intangible assets using data from different sources (Corrado et al., 2005, 2006, and 2011; Hulten & Hao, 2012). In this approach, three broad categories of intangibles are identified which have some overlap with what is commonly considered as innovation‐related activities (or capabilities): (1) computerized information (software and databases); (2) innovative property (R&D as well as intellectual property protection costs, architectural and engineering designs); and (3) economic competencies (brand names, firm‐specific human capital and organizational capital). While this approach offers a better and broader measure of firms’ capabilities, it poses a challenge as the information required cannot be obtained from existing innovation surveys. Moreover, it’s important to highlight that measures of innovation assets are not equivalent to innovation outcomes, as inputs can be used more or less efficiently. Therefore, there is some uncertainty about the type and extent of innovation outcomes that can be produced by firms using their knowledge assets.

# **2.2 The problems with subjective measurement**

As discussed above, one of the main challenges when measuring firm‐level innovation outcomes is the subjective nature of the definition of innovation used in the survey questionnaires. While in the case of “new” products, processes, organization practices or marketing methods, the main challenge is to check the reliability of the self‐reported outcome, an additional challenge arises when trying to capture the introduction of “significantly improved” products, processes, organization practices and marketing methods. The definition of “significantly improved” is highly subjective and can vary
- Dynarski, S. and Scott-Clayton, J. (2013), ‘Financial aid policy: Lessons from research’, _The Future of Children_ , 23(1), p67-91.

- Ferreyra, M., Avitabile, C., Botero, J., Haimovich, F. & Urzúa, S.  (2017), ‘At a Crossroads: Higher Education in Latin America and the Caribbean’, Washington, DC: World Bank Group.

- Dinarte, Lelys; Ferreyra, M. M., Melguizo, T., & Sanchez, A. (2022), “The Contribution of Short-Cycle Programs to Student Outcomes : Evidence from Colombia.” _Policy Research Working Paper_ 9424, World Bank, Washington, DC.

- Ferreyra, M.M, Dinarte, L., Urzúa, S., & Bassi, M. (2021). “The Fast Track to New Skills: Short-Cycle Higher Education Programs in Latin America and the Caribbean.” _Forthcoming_ . World Bank, Washington, DC.

- Field, E. (2009), ‘Educational debt burden and career choice: Evidence from a financial aid experiment at NYU law school’, _American Economic Journal: Applied Economics_ , 1(1), p1-21.

- Figlio, D., Rush, M. & Yin, L. (2013), ‘Is it live or is it internet? Experimental estimates of the effects of online instruction on student learning’, _Journal of Labor Economics_ , 31(4), p763-784.

- Filmer, D., Nahata, V., & Sabarwal, S. (2021). ‘Preparation, Practice, and Beliefs: A Machine Learning Approach to Understanding Teacher Effectiveness,’ _Policy Research Working Paper,_ WPS 9847. Washington, D.C.: World Bank Group.

- Grosz, M. (2020), ‘The returns to a large community college program: Evidence from admissions lotteries’, _American Economic Journal: Economic Policy_ , 12
Gender Pension Gaps in a Private Retirement Accounts System: A Dynamic Model of Household Labor Supply and Savings Clement Joubert and Petra E. Todd<sup>_∗_</sup>

# **JEL Classification: J240, J260, E210, E260, O170**

**Keywords:** Private pensions, Gender Gaps, Household labor supply, retirement, savings, structural estimation

> _∗_ Petra E. Todd is the Edmund J. and Louise W. Kahn Term Professor of Economics at the University of Pennsylvania and Clement Joubert is an economist at the World Bank’s research group. This research uses data from the EPS survey. We would like to thank the Subsecretar ́ıa de Previsi ́on Social of Chile for granting permission to use the data base. The Subsecretar ́ıa is not responsible for the results presented in this paper. This paper builds on an initial study that Todd and Joubert did as consultants to the Budget Office in the Chilean government. A preliminary version of the paper was presented at the LACEA meetings in Santiago, Chile, Nov., 2011, at NYU, Washington University-St. Louis, University of Minnesota, and University of Chicago. We would like to thank Alberto Arenas, David Bravo, Santiago Levy, Evelyn Matthei, Beatriz Moraga, Roy Rogers, the Superintendencia de Pensiones and seminar participants for helpful comments. We also are grateful for funding for research assistants from the National Institutes of Health - National Institute on Aging, Grant number P30 AG12836, the Boettner Center for Pensions and Retirement Security at the University of Pennsylvania, and National Institute of Child Health and Development Population Research Infrastructure Program R24 HD-044964, all at the University of Pennsylvania.
]|
|Men’s ideal number of daughters|1.18|[0.94]|
|Men’s ideal number of children|3.14|[1.47]|
|Couple’s actual proportion of ever-born sons|0.52|[0.36]|
|Couple’s actual number of ever-born children|2.54|[1.80]|
|Couple’s actual proportion of living sons|0.52|[0.36]|
|Couple’s actual number of living children|2.33|[1.57]|
|Other controls|||
|Fraction of couples residing in urban area|0.41||
|Women’s average education level|2.65|[1.34]|
|<br>Men’s average education level|2.82|[1.34]|
|Women’s average age|33.1|[8.06]|
|Men’s average age|37.1|[8.29]|
|Couple’s average wealthquintile|2.89|[1.47]|

Source: Indonesian 2007 Demographic and Health Survey (DHS). Note: Education level is defined as 0=no education, 1=incomplete primary, 2=complete primary, 3= incomplete secondary, 4=complete secondary, 5=higher.

38
approaches hold a great deal of promise; however, behavioral strategies must also be pursued. Current levels of investment in the development of new drugs, vaccines, and technologies have been at least partially fueled by the perception that behaviorallyfocused approaches cannot be made to work, or are politically and socially unviable. However, biomedical advances such as a cure for AIDS or the development of an effective AIDS vaccine are, at best, many years away or unattainable. Moreover, even once a clinic-based intervention, drug, or vaccine has been proven efficacious, changes in behavior are still needed to ensure access, uptake, and acceptability. Consequently, greater attention is now being given to a focus on behavioral change as an important component of prevention strategies and activities.

In the Tanzanian context in which the RESPECT trial was fielded, Table 1 shows that knowledge of AIDS and HIV prevention strategies is extremely high and accurate, with over 99% of respondents familiar with HIV and 85% identifying condom usage as a strategy to prevent HIV. Table 2 further shows that a large portion of young adults have been HIV tested (a national testing campaign occurred two years earlier and testing is standard during prenatal care), and people believe that HIV rates are quite high. The average respondent estimated an HIV prevalence rate of 17% among people their age in their village, indicating a high level of perceived risk. Yet table 2 also shows 20% of men admitting to multiple partners in the past month (we suspect such statistics are underreported), and close to half of participants failed to use a condom during their last sexual intercourse with a non-marital partner. These young people appear to understand their HIV risks and know how to behave to prevent transmission – yet they don‘t choose to act on that knowledge. Next we review prior experience with incentivizing individuals so as to stimulate demand for prevention.

# **Literature Review on Incentives for Health Input Behaviors and Health Outcomes**

# **_Conditional Cash Transfer Approaches_**

CCT programs which
model is estimated using monthly data with four lags (based on the AIC and SIC information criteria). The Bayesian routine we employ to estimate the model first searches for 500 successful draws from at least 1,000 iterations with 500 burn-ins; the results are based on the median of these 500 successful draws. The estimation process is standard Gibbs sampling, except that the volatility of residuals is endogenously determined. In the estimation of the model, structural shocks are assumed to have unit variance.

52 Our global and domestic shocks are motivated by theoretical studies on the sources of movements in interest rates, output, and inflation in the United States. For example, Smets and Wouters (2007) develops a model that decomposes the variation in output, inflation, and policy rates into demand shocks (including risk premium, investment, and fiscal shocks), price mark-up shocks (including commodity price shocks), supply shocks, and interest rate shocks. Del Negro et al. (2013, 2022) builds a model analysing an even wider range of shocks behind U.S. macroeconomic and financial variables.

53 Typical VAR models assume that the variance-covariance matrix of residuals is constant over time. However, this assumption could be problematic in our exercise since there are several periods with substantially heightened volatility in our long time series, such as around the COVID-19 pandemic and other major global shocks (Lenza and Primiceri, 2022). The variance-covariance matrix of residuals ∑t is allowed to be period-specific, hence rendering stochastic volatility and introducing heteroskedasticity (Carriero, Corsello, and Marcellino, 2019). εt is serially independent with zero mean and variance ∑t. We assume that ∑t = FΛtF′, where F is a lower triangular matrix with ones on its main diagonal, while Λt is a period-specific diagonal matrix whose diagonal elements Λjj,t (the time-varying variances) follow a stochastic process (Cogley and Sargent, 2005).

42
|0.66|
|DensityMiddle School Numbers – Census 2011|0.31|0.30|1.70|0.90|
|DensitySenior SecondarySchool Numbers – Census 2011|0.00|0.01|-10.20|0.50|
|Proportion of Total Population Female – Census 2011|0.50|0.50|5.10|0.70|
|Proportion of Literates Population Person – Census 2011|0.46|0.47|-9.30|0.51|
|Proportion of Non-WorkingPopulation Person – Census 2011|0.51|0.49|24.50|0.07|
|Agricultural MarketingSocietyStatus – Census 2011|0.09|0.12|-9.20|0.51|
|Observations||37|8||

29
problem of selective attrition. Although there is 23 percent attrition over 30 years, the attrition is unrelated to midwife exposure.

Finally, the identification comes from variation in allotment of midwives across communities and birth cohorts. Other programs initiated by the government at the same time can confound the results. In 1993, the Indonesian government initiated the _Inpres Desa Tertinggal_ (IDT) program. Besides the goals of strengthening the democratic institution and supporting the decentralization policy, the program aided families below the poverty line and human resource development (Shah et al., 1994). Since the timing of IDT overlaps with the timing of the midwife program, it is important to evaluate whether the exposure estimates of the midwife are robust to the inclusion of IDT exposure. Fortunately, the second wave of the IFLS has information on the availability and start time for IDT in a community. A dummy exposure measure based on the timing of IDT and birth year of children is used: it takes a value of 1 if a child was exposed to program at an age younger than four and 0 otherwise. Appendix Table S7.12 reports the results. The results indicate that midwife exposure estimates for different indices remain comparable to those without the inclusion of IDT.

# **6.3 Multiple Inference Adjustment**

Recall that this paper has presented results on four indices. It is imperative to calculate statistical significance after adjusting for multiple comparisons. Appendix Table S7.13, presents both naive p-values as well as p-values after controlling for family wise error rate (FWER).<sup>42</sup> The table shows that the _in utero_ estimates of health index and economic index for males remain statistically significant (less than 0.10) even after controlling for FWER.

There are 14 adult outcomes in this paper. Given these large number of individual outcomes, one may observe statistical significance for these outcomes by chance. To address this concern, q-values, following Anderson (2008), are calculated. Appendix Table S7.14 reports the
<!-- Start of picture text --> for the Gini index at<br><!-- End of picture text --> <!-- Start of picture text --> level<br><!-- End of picture text --> Figure 2: Lowess Curve for the Gini index at 2-digit level <!-- Start of picture text --> Lowess curve for Gini of employment, value added and exports against GDP per capita (PPP) at 2-digit level<br>Employment Value-added Exports<br>] - I<br>||I<br>|| I<br>||I<br>|| |<br>n | mn | n |<br>||I<br>|| I<br>||I<br>|| I<br>oo | = | a<br>_= || _= || _= Il<br>°°°<br>||<br>~;||~,||~‘||<br>|| I<br>||I<br>|| l<br>wo | ‘2 | * |<br>I<br>|| |<br>||I<br>wn |wn | wn l<br>o © 6 cs) oo S © % © Oo<br>SS Ss ssSs FP SsSs SF Ps sss<br>GOP per capita, PPP (constant 19385) GDP per capita, PPP (constant 1935) GDP per capita, PPP (constant 1985)<br><!-- End of picture text -->

Source: Authors’ calculations The Gini index follows a U-shaped pattern: Countries first diversify then they start specializing at around PPP GDP per capita of 20,000 (constant 1985 US dollars). This result is in line with the literature
(million 1<br>accordi<br>Census<br>Valuation<br>1977|alue<br>put<br>977 US$)<br>ng to<br>Our<br>Estimate<br>1975|Ratio<br>of 1975<br>to 1977<br>Quantity<br>for<br> <br>Gross<br>Output|
|---|---|---|---|---|---|
|2041 Flour and Other Grain Mill Products 2,968.0|2,749.4|92.6348|3,683.3|<br>3,412.0||
|2046 Wet Corn Milling<br>1,052.7|809.1|<br>76.8595|2,014.8|1,548.6||
|TOTAL|||5,698.1|<br>4,960.6|87.0568|

Source: Gross Value of Output at 1972 US$ from US Dept. of Commerce, 1982 US Industrial Outlook, Washington D.C; Census Valuation of Output at 1977 US$ from 1977 US Census of Manufactures, see table A.l1; Table A1.llb - Price Movements in the United States, Grain Mill Products. 1975-1977

|=________________________.._____________________________________________-<br>Gross Value<br>of Output<br>in 1975<br>according to<br>ASM<br>Our<br>(1975 US$) Estimate<br>(1977 US$)|Ratio<br>of 1975<br>to 1977<br>Prices<br>for<br>Gross<br>Output|
|---|---|
|2041 Flour and Other Grain Mill Products 4,327.6<br>3,412.0
store maize, only two percent do so to sell it later in the season<sup>19</sup> . Their main reason for storing maize is for household consumption.

_Table 4: Household farm size by distance to market_ _<u>(terciles)</u>_

||Iringa|Arusha|Dodoma|
|---|---|---|---|
|Lowest|3.1|2.8|4.9|
|Middle|6.9|2.6|7.1|
|Highest|4.0|2.7|6.1|

Source: Author‟s calculations from HBS 2007.

> 18 Data of the quantity of maize produced by household was not available.

> 19 Tanzania Sample Census of Agriculture 2002/03.

19
further (to levels close to Germany for example).

Resource costs using the eight country experience are seen to be highly variable in the [0-50] percent decentralization range, and they decline systematically (with the exception of South Africa) as we move to the right from [50-100] degrees of decentralization, as seen in figure 2(b) This result is in line with Oates' hypothesis that initially the central government has economies of scale in managing the financing of construction works (perhaps as observed in more efficient centralized procurement and supervision systems) and that greater decentralization results at the beginning in loss of economies of scale resulting in cost increases. The observed declining of costs corroborates Oates' contention that there may be a switch in the resource cost curve. As for the preference costs curve, there is evidence for a downward slope, which implies an improvement in the condition of roads with increasing degrees of decentralization. But as we move to the right on the graph road condition worsens (as in the case of Colombia) and then improves again. Following Biehl's (1994) diversity argument, at higher levels of decentralization, local preferences may be expressed in terms of accepting poorer road

> 16 A more in-depth analysis of Germany's experience with decentralization and its performance in the road sector is in section seven.

16
produced by poor workers likely reflects efforts by India’s trade partners to protect sectors where their own poor work. The same pattern can be seen in the tariffs on India’s imports, by matching India's own applied MFN tariffs in 2012 by sector to the income data from the survey. Goods produced by the richest workers (in the 10<sup>th</sup> decile) face a tariff of less than one-third the level on goods produced by the poorest workers (the lowest 2 deciles of the population—Table 7).

Table 7: India's import MFN applied tariffs in 2012 by income decile.

|Income<br>Decile|Applied MFN|
|---|---|
|1|35.6 c|
|2|39.0 a|
|3|37.5 b|
|4|35.3 c|
|5|33.8 d|
|6|29.9 e|

14
Moreover, the EAs sampled were spread across a vast territory and mostly in remote areas. They were far from each other, and far from urban centers. NE teams who covered rural areas had to travel up to two days to reach some EAs, longer than teams in any other region. Team leader reports from the field indicate that these large distances and conditions created fatigue among enumerators. Further, direct monitoring of field teams by supervisors was limited due to poor connectivity, and thus sending frequent and timely feedback was more challenging that for other teams. As a result, the performance of teams did not improve as in other regions.

Finally, the consumption profile of most core food items was different to other rural areas, including nearby and ostensibly similar areas covered by other teams (Table 7). Hence, the consumption data (food, non‐food and assets) were discarded, and poverty was estimated from a multiple imputation process.

# Consumption imputation process

Consumption data in North‐East rural and Jubbaland urban were imputed in Stata with Multiple Imputation (MI) techniques. The same multiple imputation process and model described to estimate the consumption of non‐assigned optional modules from equation (4) were used to obtain the four consumption components, and thus the total consumption expenditure for households in these regions.

The dependent variable of the model is total consumption expenditure per capita with data from North‐ East rural and Jubbaland urban set as missing and to be imputed.<sup>18</sup> The independent variables were chosen based on explanatory power with respect to household consumption: household size, share of children in household, share of seniors in household; household head gender, employment, and education; dwelling type, dwelling drinking water access, dwelling floor, and dwelling ownership; household experience of hunger and receipt of remittances; population type (urban, rural, IDP, nomadic) and a region‐population type interaction, as well as consumption quartiles. With an R‐Squared of 71 percent
<!-- Start of picture text --> 200<br>180 N<br>160 \<br>140 \<br>120 \<br>100 \<br>80 \<br>60 \<br>40 ° \<br>\ v WY I<br>op N N N BN IA<br>o WIPp WkDH 2. | lyODN WKBB OBby aS1S B RBB<br>1997 1999 2001 2008 2010 2011 2012 2013 2014 2015 2016 2018 2019<br><!-- End of picture text -->

- @ Newly Added Sanctioned Entity in Entity List

- & Identified Newly Added Sanctioned Firm and Affiliates in PATSTAT
|35.1|4.0|4.1|26.0|26.3|

Sources: "Tax Expenditure Statement" 2000-2004,Treasury, Australia; "Tax Expenditures" and budgetary tables, 2000-2004, Department of Finance, Canada; "Tax Expenditures" 1999-2003, Ministry of Finance, the Netherlands; and "Tax Expenditures by Function" and FY 2005 Budget, Office of Management and Budget, USA. a/ "Total expenditures" is the sum of "tax expenditures" and "direct expenditures." In Turkey (central government only) the potential tax benchmarks for PIT, CIT and VAT have been studied. A list of potential tax expenditures has been compiled that contains 186 tax expenditures relating to PIT, CIT, VAT, and special consumption tax. Most of these tax expenditures are on a large scale and are very generous. Recently, Turkey has officially estimated the cost of 15 tax expenditures provisions, which is 1.6% of GDP. However, according to various analyses and preliminary estimates from other organizations, the total cost of tax expenditures, adding the other omitted tax expenditures, would raise to at least 5% of GDP in 2003.

In China (both central and local governments), for example, tax expenditure policy has become popular since 1978, when China launched an economic transformation from a planned to a social market economy. The government has not established a tax benchmark structure, but it recognizes that most tax incentives in its system are tax expenditures. The estimated cost of tax expenditures is not available to the public, even though the central government has made estimates for about 100 tax expenditures. However, according to a reputable tax expert in China, the total number of tax expenditures is probably 1000, more or less<sup>8</sup> . The size of the tax expenditures could be well over 10% of GDP<sup>9</sup> in 2002.

# **B. The Effects of Tax Expenditures