rafmacalaba/data-use-mentions-extended huggingface dataset:
Table 9: Post-sanction Innovation Performance among Firms in the Sanctioned Technology Field

||(1)<br>**Pat**|(2)<br>**ent Applic**|(3)<br>**ation**|(4)<br>**Aver**|(5)<br>**age Colla**<br>**per Pate**|(6)<br>**borator**<br>**nt**|
|---|---|---|---|---|---|---|
||Total|Intl.|High-Tech|U.S.|Euro+|Adv. Asia|
|Treatment_×_Post|-0.069**|-0.095***|-0.035***|-0.025|0.001|0.030**|
||(0.026)|(0.011)|(0.012)|(0.015)|(0.004)|(0.010)|
|No. Obs|193,851|193,851|193,851|158,421|158,421|158,421|
|R-squared|0.410|0.425|0.481|0.409|0.269|0.390|
|Firm FE|Yes|Yes|Yes|Yes|Yes|Yes|
|Sector-year FE|Yes|Yes|Yes|Yes|Yes|Yes|

Note: We estimate the coefficients using OLS. Estimates from (4) to (6) are based on firms that started patenting before 2013. Robust standard errors clustered at the 2-digit ISIC sector level are in parentheses. ***, **, and * indicate significance at levels 1 percent, 5
years 1983, 1993 and 2004/5, National Sample Survey data show a clear gradient in poverty by city size. This gradient holds both at the national

> 21 Tarozzi and Deaton (2007) have recently expressed a concern that the small area estimation procedure employed by ELL (2002, 2003) may overstate the precision of local level poverty estimates. They base their argument on Monte Carlo simulation results. Elbers, Lanjouw and Leite (2008) examine this issue with data for the state of Minas Gerais in Brazil, and find little evidence in that specific setting for concern. It remains, though, that the ELL procedure _estimates_ poverty, rather than directly _measuring_ it, and as such there is interest in assessing whether the findings reported in this paper would also hold had poverty been directly measured.

> 22 Every five years the NSS fields a “thick round” with a sample size of around 120,000 households, The “thin rounds” fielded in the other years have sample sizes of around 30-40,000 households.
recorded in the CRs.<sup>7 , 8</sup> This contradictory move by tax authorities provoked friction with taxpayers, criticism from the public, and distrust against government policies and authorities. In addition, consumers’ tendency to neglect to obtain receipts did not change substantially. Despite compensation for obtaining CR receipts, consumers did not actively do so because the application process for receipt compensation was cumbersome and inconvenient and the economic benefit was relatively small.<sup>9</sup> Even worse, finding the system ineffective, the Korean government abruptly abolished CR receipt compensation after only three years, thus eliminating the only incentive for consumers to learn over the long term the benefit of obtaining receipts.<sup>10</sup> Distrust between taxpayers and tax authorities, low tax morale of taxpayers, repeal of receipt compensation, and even functional defects in CRs eroded the effectiveness of the cash register policy. Following the presidential and parliamentary elections in 1978, tax authorities sought to reduce friction with taxpayers, and in the early 1980s they reduced and almost totally suspended investigation into and penalties for failure to issue receipts.<sup>11, 12</sup> In 1988, the mandatory use of cash registers was _de facto_ abolished, on the reason that it had been enforced for the convenience of tax administration and that it in fact discouraged issuing and receiving receipts, despite its original objectives,<sup>13</sup> after a number of swings between strong enforcement and soft encouragement. In 1993, the article in the VAT law making CR use mandatory was officially deleted and changed to support voluntary use of CRs.<sup>14</sup> Tax credits to business sellers for issuing CR receipts (0.5 percent of concerned sales) were maintained to encourage voluntary CR use. In 1996, however, even the tax credit for CR receipts was repealed as CRs had proven ineffective in reducing underreporting of cash sales.<sup>15</sup>

> 7
Start of picture text --> Doing Business Trading on Time Index Logistics Performance Index<br>Mauritius<br>Madag Tanza sc ni a r Seychelles<br>MozambiqueLesotho South Africa South Africa<br>SwazilandNamibia Botswana<br>Congo Mala w iZambia Co n go M adagascar Tanzania Mauritius<br>Zimbabwe Angola M oz am Zambiabique Angola Botswana<br>Namibia<br>4 6 8 10 12 4 6 8 10 12<br>ln(GDP per Capita), 2005 PPP ln(GDP per Capita), 2005 PPP<br>7<br>6<br>5<br>4<br>3<br>2<br>5<br>4<br>3<br>2<br>1<br><!-- End of picture text --> Note: The World Bank Doing Business (Trading on Time) indices are constructed as follows: The underlying variables (export and import costs, time and documents) are first standardized (mean 1 and variance 1). The simple average of these is then converted to a 1 to 7 scale (1 corresponds to worst outcome, 7 to best outcome). The underlying trading times, documents and costs are the most recent values available from the World Bank Doing Business Indicators (accessed 1 June 2010) and the Logistic Performance Index (2009).

24
--|total<br>credit,<br>% of gnp<br>-------------------------------------|credit to<br>agriculture,<br>7. of agricul-<br>tural product|credit to<br>agriculture<br>% of net|credit to<br>industry<br>capital<br>__--|
|---|---|---|---|---|
|1969|31|48|19|52|
|1974|48|76|35|69|
|1979|57|101|48|75|
|1984|57|177|67|70|
|1986|67|182|79|64|

---------------------------------------------------------------------

Source: Credit balances from Annual Bank Statistics, various issues. GNP and agricultural product from Statistical Abstract, various issues. Capital stock data from Buch (1984) and privately communicated updates

# TABLE 8 SHARE OF AGRICULTURAL CREDIT HELD BY MOSHAVIM AND SUPPLY COOPERATIVES

(Selected years, end of year data, current values) r

|Year|moshavim<br>percent|supply<br>cooperatives<br>percent|
|---|---|---|
|---------------------------|--------||
We find that higher branch and ATM penetration and wider use of loan services are associated with lower financing obstacles, even after we control for a standard measure of financial sector depth. We confirm these findings when using firm-level observations and controlling for firm characteristics.

Notwithstanding the novelty of the indicator database, it is important to be cognizant of its limitations. First, unlike indicators used in the finance and growth literature, our data are only available at one point in time. This prevents us from exploring the relationship between financial outreach and economic development over time and from exploiting within-country variation in banking system outreach. Second, our data and analysis focus exclusively on two banking services, deposit-taking and lending, and thus abstract from other important financial services, such as payment and insurance, for which data are harder to get. In addition, we concentrate on banks and, therefore, we do not take into account other financial service providers, such as microfinance institutions or cooperatives, due to the scarcity of data on these institutions. Third, our indicators are crude indicators of outreach that do not take into account subtleties such as new delivery channels or more detailed indicators of loan and deposit size distribution. Fourth, our indicators are quantity indicators and do not capture the price dimension of outreach. Fifth, our indicators measure equilibrium outcomes, affected by both demand and supply factors. Finally, our indicators might be subject to mis-measurement, e.g. if bank clients have several deposit or loan accounts. In spite of these shortcomings, we see this data compilation effort and the associated analysis as a useful and important first step towards developing more accurate indicators of access to and use of financial services.

The remainder of the paper is organized as follows. Section 2 describes the data collection and introduces our indicators of outreach. Section 3 discusses the cross-country 6
 The higher flexibility offered by remote data collection tools should also be exploited to **investigate migration-related outcomes of different types of climate shocks** that might interest survey locations at large-scale, including fast-onset ones. For example, these data collection tools can be quickly adapted to conduct a partial survey focused only in the areas of a given disaster, which can then be merged with other data waves without compromising the implementation or representativeness of the core survey. This work should build upon the lessons learned from recent experiences with the COVID-19 pandemic,<sup>30</sup> which have provided actionable tools such as rapid phone-survey modules that may outlive the pandemic itself.

  As the use of remote data collection technologies comes with multiple statistical challenges (i.e., non-representativeness, attrition, non-response, and selection bias) more research should be conducted to **assess the extent of statistical challenges and validate methods for overcoming them** . Again, starting with and moving beyond the COVID-19 experience may be the key to solving challenges related to overly specific migration-related survey tools (for instance, by complementing standard face-to-face representative surveys with _ad hoc_ followup phone-survey interviews for selected subgroups, and then analyzing differences with the full face-to-face sample to econometrically address selection bias issues).

Improving **tracking** in longitudinal household surveys is also key for more accurate migration measurement, especially for internal migration. The primary benefit of an effective tracking strategy is to reduce attrition in panel surveys. But if the right information is collected as part of tracking (for example, about the motives of moving<sup>31</sup> ), it can also provide direct observation of migratory flows of households and individuals within the original survey sample. This is also an area where the use of new technologies and data sources can be highly beneficial. In general, augmenting panel survey data with new types of data like digital trace data would allow the collection of information in domains that are normally arduous to cover in household surveys due
*Stylized Facts**

# Data

This study used the Labor Force Survey conducted by the Central Agency for Public Mobilization and Statistics (CAPMAS) for the first quarter of year 2010. The survey has over 60 questions, clustered in three sections: 1) demographic and professional status (28 questions); 2) employed characteristics (26 questions); and 3) unemployed characteristics (5 questions). The survey has 88,000 respondents.

2
increase in government spending raises real GDP statistically significantly over the long term when government debt is at its (country-specific) minimum and has only insignificant effects when government debt is at its (country-specific) maximum.

In general, these conditional multipliers suggested by our results as well as previous studies conditioning on government debt are somewhat smaller than those estimated in studies that condition on other factors (Table 2). Especially recent studies that condition on recessions have found at times very large multipliers in advanced economies. Several have estimated peak fiscal multipliers during recessions in the range of 3-4 (Bachmann and Sims 2012, Auerbach and Gorodnichenko 2012 and 2013, Candelon and Lieb 2013). In a literature survey of earlier U.S. evidence, Ramey (2011) puts multipliers during a severe recession “at the upper bound of this [0.8-1.5] range,” notwithstanding considerable uncertainty about the estimates. Much of this evidence rests on U.S. data. However, in Europe as well, during the 2009-11 recessions in the wake of the global financial crisis, fiscal stimulus packages have been attributed with multipliers above 1 (Blanchard and Leigh 2014).<sup>25</sup> When we condition our multiplier estimates on recessions as well as government debt, we also find that multipliers can be above 1, at least for countries in the bottom quintile of government debt.<sup>26</sup>

# 6. Conclusion

A growing literature has documented how weaker government finances reduce the effectiveness of fiscal policy, i.e. fiscal multipliers. In this paper, we complement this literature by analyzing the empirical relevance of the two theoretical channels through which the fiscal position impacts the size of fiscal multipliers. Specifically, we study the Ricardian channel where households reduce consumption in anticipation of future fiscal adjustments during times of high debt; and the interest rate channel where increased investors’ perception of credit risks, raises sovereign credit risk and economy-wide borrowing cost, thereby weakening private domestic demand. We deploy
work.  Owing to social norms and other time burdens, rural women are more likely to underreport this work.  Recovery questions have also been shown to be particularly effective in re-classifying women as contributing family workers, when they initially report not being employed.

# III. Labor underutilization and time use

## Labor underutilization

Given the narrower definition of employment under 19th ICLS, and the role of seasonality in rural jobs, the 19th ICLS recommends collecting data on two measures of labor underutilization — time-related underemployment , and the potential labor force — to better understand outcomes for individuals who would like to work more, or are out of the labor force.

Time-related underemployment refers to whether individuals in employment, over a short reference period (a) wanted to work additional hours, (b) whose working time in all jobs was less than a specified hours threshold, and (c) who were available to work additional hours given an opportunity for more work (ILO, 2013; para 4).<sup>4</sup> Time-related underemployment has significant bearing on women as well; often when women get paid jobs, for example, their household responsibilities do not decline, and they are more likely to work part-time even if they wanted to work more (Folbre, 2018).

> 4 The 19th ICLS also provides guidance on how to construct different thresholds based on working hours (ILO, 2013).

10
fixed costs of sourcing from another country. Thus, source countries can be complements or substitutes, depending on the firm’s demand elasticity. Using US data, Antras, Fort, and Tintelnot (2017) also present evidence that countries not only differ in the cost of inputs, which lowers production costs, but have different levels of fixed costs of importing.

Given the significance of the global sourcing decision for firms, it is noteworthy that there is substantial heterogeneity amongst otherwise similar firms in both the decision to source inputs internationally and the foreign markets that they choose to source from. One explanation for this variation is that the costs associated with initiating a new sourcing arrangement in a foreign market are not known with certainty by the firm and must be inferred from outside sources. In this context, one of the most valuable sources of information about foreign markets for a firm is neighboring firms’ experiences in those markets. The empirical literature has found that these informal barriers to trade help explain the observed firm-level heterogeneity in exporting and that firms learn about foreign markets from neighboring exporters. The earliest studies of this effect found mixed results, for example, neither Aitken, Hanson, and Harrison (1997) nor Bernard and Jensen (2004) find evidence for export spillovers specifically. However, later research refined the scope of potential export spillovers to be market-specific, which is more aligned with international trade theories that feature heterogeneous firms (e.g., Melitz (2003)). Koenig (2009), Koenig, Mayneris, and Poncet (2010), and Fernandes and Tang (2014) find a strong learning effect for exports by using data that allow for a firm’s neighbor set to be more specifically defined in terms of smaller geographic and industry groupings as well as more detailed customs data that allows for the measurement of market-specific spillovers. However, the focus of this literature evaluating how networks and information spillovers reduce informal trade barriers has been to explain heterogeneity in firms’ export decisions. Papers on import learning are limited
concerns and individual protection issues rather than attempting to enhance credit information per se. Monopoly pricing or other restrictive practices that might limit competition or service provision are generally the responsibility of competition authorities that have in practice rarely intervened in this specialized sector. The second form of intervention that has taken place is the direct (and frequently forced) provision of credit information sharing services by central banks or banking supervisors known as public credit registries (PCRs).

World Bank surveys have documented that PCRs exist in about 60 countries worldwide and more nations are planning to create them in the future.<sup>4</sup> In countries with PCRs, supervised financial institutions are required to provide data on individual borrowers on a periodic basis, usually monthly. Core PCR data are information on the identity of borrowers, the size of any loans or credit lines outstanding with reporting institutions and their status. Status implies whether a loan is in good standing, past due, in default or other non-accrual status.

There are multiple motivations behind the development of PCRs. Recent research has linked the existence of a PCR with the absence of significant private credit bureaus, weak creditor protection and a French civil code legal system – see Jappelli and Pagano (2003) and World Bank (2003). Anecdotal evidence suggests some countries originally developed PCRs to monitor credit by sector in the days when banks were forced into particular lending patterns. Others were motivated by the simple desire that financial institutions should know the total debts (and the standing of the various loans) of potential borrowers in the whole financial system to make informed lending decisions. Another motivation was so that bank supervisors could identify the main debtors of the financial system and analyze more carefully loan concentration risk and monitor and enforce banks’ reserve policies against problem loans.

Whatever the primary motivation, countries that have developed these tools have tended to find them useful for a wider variety of reasons that often go beyond their original thinking. This paper reviews many of the current uses and discuses the relationship between these
deviation observed in the control group.

In addition, we access separate administrative data on students’ records that we use to construct an indicator for enrollment in seventh grade, which is the first grade in lower secondary school. The sample reduces to 468 sixth graders in 182 schools, who are deciding whether to transit to secondary school. This sample reduction is due to the multi-grade aspect of the schooling system, where student composition among grades in each school is not homogeneous in size. Missing schools in this analysis are balanced among treatment arms. The choice of this cohort of students is meant to maintain the same length of exposure to the API program of the main sample of the analysis.<sup>5</sup> Finally, we conducted a series of in-depth interviews in the spring of 2022 for a small and representative subsample of 16 mentors and 12 community instructors who were part of our study.<sup>6</sup> This qualitative evidence proves useful to complement the quantitative analysis and to shed further light on the mechanisms through which the mentoring intervention affects students and parents as discussed in Section 3.3, as well as on the role of parents as means of scalability as discussed in Section 5.2.

# **3 Experimental Evidence**

In this section, we report OLS estimates from separate regression models for each experiment on the treatment assignment indicators for the _Original_ and _Plus_ modality after two years of exposure to the mentoring program. All models include the strata indicators that account for the block randomization design (see Section 2.2) as well as few individual characteristics such as students’ age and ethnicity. We control for interview week fixed effects, which account for changes in weather and political conditions, as well as indicators for the different teams of enumerators who administered the survey across the communities in our sample. The error terms are clustered at the school level, which represents the unit of randomization of the treatments. We complement the usual asymptotic inference with two alternative procedures. First
Figure A10: Heterogeneity in poverty gradient across countries, DOU <!-- Start of picture text --> MWI<br>NER<br>UGA<br>AGO<br>LSO<br>BFA<br>GHA<br>TCD<br>GNB<br>SEN<br>TZA<br>GIN<br>CIV<br>ETH<br>MRT Urban center<br>Urban cluster<br>GAB<br>−.6 −.5 −.4 −.3 −.2 −.1 0 .1<br>Regression coefficients and confidence intervals<br><!-- End of picture text --> Source: International Urban Poverty Database. Each dot represents the regression coefficients _DOUj_ ( _i_ ) of equation 5 for each country _c_ , excluding control variables. Confidence intervals are represented with a line. The dependent variable _POVi_ is a dummy variable taking the value 1 if a household’s per capita expenditure expressed in PPP and spatially deflated falls below the poverty line. _DOUj_ ( _i_ ) is a vector of dummy variables: "Urban cluster" ("Urban area") takes the value 1 if household _i_ lives in an "Urban cluster" ("Urban area") as defined by the DOU method. The baseline category is "Rural". WorldPop 250m is used.

34
.

## _5.1.2 Top origin countries and preferred destinations_

In each survey round, for each FMP, DTM data report the three most common migrant nationalities registered and the three most preferred destination countries for the migrants located at that FMP.

Table 2 shows the five nationalities most commonly registered at the FMPs in 2017 and 2018. The order of the nationalities indicates how frequently they were found.

Table 3 shows the five more preferred arrival destinations registered at the FMPs in 2017 and 2018.

> 9 Baseline Assessment data are repeated estimates of net stocks of migrants at the FMP level. This implies that there is the possibility of double counting (counting the same migrant at two different FMPs during a given time period). There is a tradeoff; using the monthly data would minimize the probability of double counting (it is unlikely that the same individual would move between FMPs over such a short time period), but high frequency data may be affected by a high level of variance, which would make difficult to analyze longer-time trends in the evolution of migration flow. For this reason, we decided to use the yearly average of the monthly data at the FMP level (which are themselves an average of data collected each two weeks).

9