rafmacalaba/data-use-mentions-extended huggingface dataset:
were newly developed.

# **VII External Resources**

This section provides information on external resources available for the study, including the location of the described data. For each resource, contributors can provide a _type_ and free-form _description_ , _citation_ , _link (DOI/URL)_ , and information on the _access policy_ for this resource.

If available, contributors should not only point to the data itself, but also to resources describing the data and study such as academic publications, reports, codebooks, ethics protocols, etc. The controlled vocabulary for the resource _type_ is adapted from the World Bank Microdata Catalog but allows as additional types “database or data repository entry” (to describe the locations of the study’s datasets), “trial registration”, “pre-analysis plan”, “populated pre-analysis plan”, and “research ethics documentation”. The last four entries complement the information in section VI.

Data from a single RCT may be stored in different locations (e.g. replication data in a repository like Harvard Dataverse, access-restricted identifying data with the researcher, administrative data with a separate data provider); each resource should be linked. Conversely, different external resources, such as the data itself along with replication code, are sometimes stored in the same location or with the same citation information. The CV allows contributors to enter the information once and tick all options that apply under _type_ and provide additional detail in the _description_ .

# **5 Creating a Metadata Catalog Based on the Metadata Schema**

We close with a few considerations for implementing a catalog based on the proposed schema, with a focus on maximizing the quality, consistency, and usability of the metadata and catalog.

**Catalog:** An RCT metadata catalog consists principally of a back-end data entry interface; front-end search, filtering, and display functions; and an underlying database of metadata fields. Catalog owners may consider modifying the schema. For users interested in adopting only a subset of the metadata fields, we
in that sector. So on average, transportation industry appears healthier because largest firms in that industry are healthier.

Figure 2 below contrasts proportion of firms with ICR below 0.75 and the UDR.

**Figure 2.  Proportion of Firms with ICR below 0.75 and the Total UDR**

<!-- Start of picture text --> Proportion ICR and UDR in 2006, by sector<br>35%<br>30%<br>25%<br>ICR<0.75<br>20%<br>15%<br>UDR<br>10%<br>5%<br>0%<br>4. 5. 8.<br>1. Mining<br>2. Industry Hotels Energy Estate<br>3. General Trade/Services Construction Transportation 6. Tourism and 7. Utilities and Financial/Real<br><!-- End of picture text --> Source: ASE data, own calculations.

# **4. Shocks to Corporate Vulnerability**

In this section we use corporate data to evaluate of the impact of shocks on corporate ability to repay debt. The ICR and UDR are used as two metrics to see the impact on corporate vulnerability of shocks to corporate sector.

# **4.1 Interest Rate Shocks**

Here we investigate two possible scenarios for interest rate shocks: increase in interest rates by 4% or 8%. These two shocks would directly affect corporate ability to repay their debts, and be reflected in the proportion of firms with ICR below the threshold and the sectoral level UDR.

To evaluate the impact of shocks the following procedure is adopted.

1. Calculate current imputed interest rate by dividing reported interest expense over estimated total debt.

18
who answers the questions. Household surveys in developing countries typically ask the head of the household, usually the male spouse, to answer employment questions about all household members.<sup>14</sup> Proxy respondents may not always provide accurate information, and this may bias the obtained statistics on employment and their distribution (Hussmanns, Mehran, and Verma, 1990). One alternative is to ask individual household members above a certain age directly; as do the Living Standards Measurement Study surveys (LSMS)<sup>15</sup> and Labor Force Surveys (LFS). However, requiring self–reporting from all individuals puts an extra logistical and financial burden on the fieldwork, and in practice survey managers often face a trade‐off between information accuracy and the cost to obtain it.<sup>16</sup> An experimental study for the US investigated the potential bias of proxy versus self‐response for health statistics, finding that randomly selected subjects reported fewer health events for themselves than for other household members (Mathiowetz and Groves 1985). In earlier analysis, we assess the implications of survey design for descriptive statistics and find important effects of proxy versus self‐reporting on resulting labor statistics in Tanzania, observing that reporting by proxy leads to lower male employment rates, mostly due to underreporting of agricultural activities (Bardasi et al 2011). This bias is reduced when the proxy respondent are spouses or have some schooling. The consequences of these observed differences in categorization for the estimates of structural parameters, such as the returns to education, remain, however, unknown.

# **3. Experimental design and estimation strategy**

Given the limited evidence of the effect of survey design choices on returns to education estimates, the survey experiment focused on the two key dimensions discussed above: (i) the level of detail of the questionnaire, more specifically the screening questions to establish employment status, and (ii) the type of respondent, namely self versus proxy response. Households were
responses are around 1 percentage point and the peak responses are somewhere around 2 to 3 percentage points for all identification methods using annual data (panels A and C). When we use the quarterly data (panel B), the response to the inflation shock identified by the residual from an estimated Philips curve is smaller (the initial response is around 0.5 percentage points and the peak response is around 1 percentage point)<sup>12</sup> . Compared with the results of the simulation approach, these responses are generally larger and more persistent. This may imply that some secondary effects through real GDP or primary balance are captured in the estimation approach but not in the simulation approach. Another possibility is that the shocks identified in the estimation approach have somewhat persistent effects while the shock in the simulation approach is purely temporary.

Regarding the responses to the high inflation and deflation shocks identified by certain thresholds (the third identification method), we can see clear asymmetry between the impacts of inflation and deflation shocks and the latter is much larger than the former (panel D). This is probably because the downward adjustment of nominal interest rates is more sluggish (downward rigidity) in response to a deflation shock.

# **C.   Robustness checks**

Following the literature on the local projection, we examine two types of robustness checks:(1) controlling the lead variables and (2) dealing with “Nickel bias” for short time-series dimension.

**(1) Controlling the lead variables.** According to Teulings and Zubanov (2014), the local projection without controlling for the shocks in the regressors between periods t and t+h when estimating the impulse response at horizon h could bias the impulse response. For the robustness check, we add the lead variable for the inflation shock to the baseline specification.

Figure 4.2 shows the results, which are broadly in line with the baseline results.

**(2) Nickel bias for annual data** **
other food types. Consequently, the analysis compares the design error as estimated above with respect to item-specific features such as consumption incidence (i.e. common or rare items), consumption value, the share of consumption from home production, the frequency of market purchase of a food item, and the storability or perishability of the good. This analysis unveils some of the mechanisms underlying the misreporting and enhances the relevance of our results beyond the specific context of our survey experiment.

# **V. Results**

## _Survey design and the report of consumption incidence_

The consumption decomposition results begin with table 3, which illustrates the consumption incidence for 12 food groups relative to the benchmark module. The consumption incidence estimated by the benchmark is given by the constant term. Several lessons are immediately apparent, beginning with the relative performance of the 7-day and 14-day recall modules. These recall modules record significantly lower consumption incidence among most food groups. For example, while 67 percent of benchmark households

> 11 The results are appreciably similar if binary response models (probit or logit) are used in place of ordinary least squares.

9
training sample is reduced. Results from random forest models produce similar results to the LASSO-based regressions, and corroborate the results across different types of high-resolution resolution-types and sectors.

Finally, we obtain results from a similar Poisson model that uses estimates of population density obtained from the 2012/13 HIES instead of the census. We then compare the out-ofsample predictions from this model with the actual census population densities in villages not covered by the HIES. This yields an out-of-sample _R_<sup>2</sup> of 0.79, Spearman Rank Correlation (SRC) of 0.91, a mean Relative Error (RE) of 37%, and a median RE of 28%. Based on _R_<sup>2</sup> and mean RE, our model is more accurate than the top-down measures: GPW (both years), GHSL, LandScan, and the 2012 WorldPop, and not as accurate as Facebook and WorldPop 2015.<sup>4</sup> The results on these relative accuracies extend to village-level population-count predictions derived by multiplying density predictions by village area. The prediction accuracy remains similar if only indicators that are publicly available are used in the model.

> 3 Rural buildings are mostly residential, but urban buildings tend to contain a heterogeneous mix of commercial and residential buildings. High resolution built-up area measures and object identifiers are better able to capture these heterogeneities in urban areas.

> 4 Using median RE as the benchmark, we report comparable performances to all 2015 estimates, except Facebook and WorldPop 2015.

3
# **3.4 Empirical specifications**

For our main specification, we use monthly panel data between June 2019 and May 2021, spanning the two years after participants graduate. For formal sector outcomes, including wages and contract type, we use the administrative IMSS data. For informal sector outcomes, we reconstruct the panel data from the employment history reported in the endline survey.

As our main specification, we follow Groh et al. (2016) and estimate: where _yit_ is the outcome of interest in time period _t_ for person _i_ . _Treati_ is equal to one if person _i_ received the offer of a wage bonus at baseline. _θi_ is a set of stratifying covariates including gender and school type, and _λt_ present month dummies. We estimate regressions with and without controlling for covariate vector _Xi_ . Standard errors are clustered at the individual level. The coefficients of interest ( _β_ ) are intent-to-treat estimates and should be interpreted as the average effect of being _eligible_ for the wage incentive over all periods _t_ .<sup>21</sup> We also estimate specification 1 separately for each month _t_ to test how treatment effects evolve. We present these monthly results graphically.

Second, to estimate effects on aggregate outcomes _yi_ , e.g. the total number of months employed, we estimate: with _β_ measuring the cumulative effect over the evaluation period.

Third, to analyze effects on job retention we apply the Anderson-Gill (AG) extension of the Cox proportional hazards regression model that accounts for recurrent events and discontinuous risk intervals associated with multiple entries into and exits from work (Andersen and Gill, 1982). In this specification, the hazard of exiting employment is:

> 21Similar to Groh et al. (2016), we do not report the treatment effect on the treated since being offered the voucher may affect job search and other determinants of employment outcomes.

19
, and also because the macroeconomic environment and economic growth variables show a sizable degree of variance. In addition, Poland has struggled in the last few years to reduce poverty while still experiencing positive but subdued economic growth.

# **A. A SUCCESSFUL MACROECONOMIC PERFORMANCE**

Economic growth has not been uniform over the last decade. After years of growth above 5 percent or above in the mid 1990s, growth declined substantially to reach a meager 1 percent in 2001, only to increase again in 2003 with good prospects for the coming years (see Figure 1 and Table 1). The slowdown in growth was accompanied by an increase in poverty and unemployment as is evident from Figure 1.

This section provides a brief discussion of recent economic developments in Poland. The Polish Central Statistical Office’s annual Household Budget Survey (HBS) micro-data sets (1994-2002), as well as publicly available real sector and monetary macro data, have been used to analyze linkages between the macroeconomic environment on the one hand and poverty, inequality, and consumption distribution trends on the other.

> 1 The 10 new member states that joined the EU on May 1 2004 are the Czech Republic, Cyprus, Estonia, Hungary, Latvia, Lithuania, Malta, the Slovak Republic, Slovenia, and Poland.

2
J.C. Duque, N. Lozano-Gracia, J.E. Patino, P. Restrepo / LCR Flagship Report on Cities and 35 Productivity Anderson, SJ; Tuttle, BT; Powell, RL; Sutton, PC (2010). Characterizing relationships between population density and nighttime imagery for Denver, Colorado: issues of scale and representation. _International Journal of Remote Sensing_ , 31(21): 5733–5746. https://doi.org/10.1080/01431161.2010.496798

- Angel, S; Sheppard, SC; Civco, DL (2005). The dynamics of global urban expansion. Transport and Urban Development Department, The World Bank, Washington, DC.

Angel, S; Parent, J; Civco, DL (2010). Ten compactness properties of circles: Measuring shape in geography. _Canadian Geographer_ , 54(4): 441–461. <u>https://doi.org/10.1111/j.15410064.2009.00304.x</u> Batty, M (2008). The Size, Scale, and Shape of Cities. _Science_ 319, 769.

Batty, M; Longley, P (1994). Fractal Cities: A Geometry of Form and Function Academic Press, London.

- Billor, N; Hadi, AS; Velleman, PF (2009). BACON: Blocked adaptive computationally efficient outlier nominators. _Computational Statistics & Data Analysis_ , 34: 279–298.

- Bleakley, H; Lin, J (2012). Portage and Path Dependence. _Quarterly Journal of Economics_ , 127: 587644.

- Boeing, G (2017). OSMnx: New Methods for Acquiring, Constructing, Analyzing, and Visualizing Complex Street Networks. Available at SSRN: <u>https://ssrn.com/abstract=2865501</u>

- Bright, EA; Rose, AN; Urban, ML (2013). LandScan 2012. Oak Ridge, TN: Oak Ridge
( _G_ 1) observed at the time the program was about to start: the baseline (ENCASEH 1997). The coefficient _η_ 2, is essentially one-minus-the coefficient of intergenerational persistence as it indicates the stability of outcomes across generations. In this formulation we are also interested in both _η_ 1 and _η_ 3, as the first is the baseline effect of the policy with respect to control individuals or households while _η_ 1 + _η_ 3 is the corresponding parameter for the early treated. If we take equation (4) and replace _Y_ ( _G_ 2) _ij_ (2017) with _Y_ ( _G_ 1) _ij_ (2017) (parents’ outcome observed in 2017), _η_ 1 and _η_ 3 would give information about lifecycle effects for the treatment group. Following Chetty et al. (2014), in this analysis we employ as outcomes the relative position of the household using the percentile of three different measures: schooling, a durable asset index and income. Each percentile is calculated within groups formed by 10-year window age ranges.

# **V.B Analysis Using the Intercensus**

To avoid the small window provided by the early versus late treatment identification, we complement the previous analysis with a difference-in-difference (DiD) strategy that employs the program’s roll-out and different age groups. For this purpose, we create four groups of localities based on their enrollment year into PROSPERA: (i) E1, which indicates localities enrolling 17
highly persistent over time, implying that the pre-democracy elite’s political power can potentially also persist over time in a democracy. The following two subsections investigate two features of malapportionment that could further allow pre-democratic elites to maintain political influence over time in a democracy.

# **4.3 Malapportionment and Political Competition**

This section uses within country data for a number of Latin America countries to study the relationship between legislative malapportionment and political competition. We rely on _within_ country data since our theoretical argument suggests that malapportionment shifts the distribution of political power across regions within countries, which may also affect the degree of political competition.

Our measure of political competition is the Herfindahl-Hirschmann index of political concentration (HH index). We calculate this index using the share of votes going to different political parties (or coalitions of parties) in different regions<sup>25</sup> . An increase in this index denotes an increase in the degree of political concentration. The first two columns of Table 7 display the results of running regressions similar to Equation 3 but using the HH index as the dependent variable. Columns 1 and 2 include regressions without and with controls, respectively. The results show a positive relationship between malapportionment and the degree of political concentration. However, the coefficients in the regression with controls are only marginally statistically significant (p-value of 0.15). In terms of economic significance, a one standard deviation increase in overrepresentation leads to an increase of about 0.09 standard deviations of our measure of political concentration.

To further study this mechanism and to relate it to the results in Table 4, we examine the effect of malapportioment on an interaction term between the HH index and the vote share going to parties associated with pre-democracy regimes. The idea here is to study whether political concentration in overrepresented areas favors parties that are closer to former non–democratic

> 25 We constructed the HH index for Argentina, Bolivia, Brazil, Chile, Colombia, Ecuador, Guatemala, Honduras, Mexico, Panama, Paraguay
observable characteristics among surviving firms. Although a part of the training was intended to encourage women to expand into other lines of business, in the round 5 data we find only a small and statistically insignificant impact of the training or training plus grant on change in product lines, introduction of new products or services, or starting a second business since baseline.<sup>15</sup> Given these findings, we proceed to analyze business outcomes for the group of surviving firms.

Table 4 examines whether the improvements in business practices following the training, along with the cash grants, lead to improvements in business outcomes. We examine the impact of the two treatments on monthly profits (panel A), monthly sales (panel B), and capital stock (including inventories and raw materials but excluding land and buildings, panel C). Panel D examines changes in hours worked in the business by the owner. For each outcome we first show the results using all follow-up rounds pooled together to achieve greatest power, and then examine the effects round-by-round. Profits, sales and capital stock are all noisy variables, and so in addition to showing the impact on the raw levels in column (1), we use two measures which are less susceptible to outliers. The first, in column (2), involves truncating the data at the 99<sup>th</sup> percentile of reported levels. The second, in column (3) takes the log of the outcome of interest. In panel A, we report both the ITT and the TOT for profits. For sales, capital stock, and hours we just report the ITT estimates. Approximate TOT estimates can be obtained by dividing the ITT by the proportion of firms attending some training (0.70). Finally, Figures 4A and 4B show the cumulative distribution functions of profits by treatment status for round 2 and round 5 respectively. These allow us to better assess in which parts of the distribution effects occur.

The results show that business
This study examines the factors that contribute to student learning outcomes in Argentina, using data from PISA for years 2000, 2006, 2009, and 2012. We estimate a year-specific production function whose results are then used to explain the trends in learning outcomes in terms of changes in inputs and changes in parameters linked to each of them (the _efficiency effect_ ). The paper is organized as follows: Section II presents a brief review of the literature. Section III describes the trends in learning outcomes using four rounds of PISA: 2000, 2006, 2009, and 2012. Section IV presents the methodology, including the education production function and the microsimulation used to decompose the changes in learning outcomes in terms of inputs, parameters, and residuals. Section V presents the findings. Finally, Section VI offers reflections on implications for education policy in Argentina.

# **II. Literature Review**

Learning is a complex, multi-faceted process. There are two types of factors that influence learning: those that are endogenous to the education system, that is, within the sphere of influence of education policy makers, and those that are exogenous, or beyond the sphere. This section cites some of the conventional exogenous factors identified by the literature and briefly summarizes the evidence on endogenous inputs, dividing them into two broad groups: school characteristics and institutional setting.

The evidence linking learning outcomes with exogenous factors such as socioeconomic background and individual characteristics is vast (Coleman et al., 1966, Lee and Barro, 1997, Fertig and Schmidt, 2002, and many others). There is some evidence that, in general, socioeconomic status is one of the most important determinants of learning outcomes (Mizala, Romaguera, and Urquiola, 2007, and others) but relatively less important than school factors in developing countries (Heyneman and Loxley, 1983, Fuller, 1987, Baker, Goesling, and Letendre, 2002, and others). With regard to parents’ education, the literature also points to this factor as being correlated with student
Schultz, T.W. 1975. "The Value of the Ability to Deal with Disequilibria." Journal of Economic Literature, 13 (3)M 827-846.

Shrestha, G., et. al. 1983. Determinants of Educational Participation in Rural Nepal. Khatmandus Tribhuvan University, Research Center for Educational Innovation and Development.

- Stelcner, H., A-M. Arriagada, and P. Moock. 1987. Wage Determinants and School Attainment Among Men in Peru. Living Standards Measurement Survey, 138. Washington, D.C.: World Bank.

UNESCO. 1987. Statistical Yearbook. ParissUNESCO.

Willis, R. and S. Rosen. 1979. 'Education and Self-Selection." Journal of Political Economy, 87(!,II): S7-S36.

World Bank. 1973. Appraisal of First Education Project.

- World Bank. 1981. Peru Basic Education Subsector Memorandum. #3484-PE.

World Bank. 1983. Sector Strategy Paper.

World Bank. 1988. Educational and Social Indicators Data Bases.<sup>2</sup> 1/ 21/ Education data in the World Bank data bases are supplied by UNESCO.

49