rafmacalaba/data-use-mentions-extended huggingface dataset:
## **2.1.2 Deep Trade Agreements Database**

Against the trend of increasing restrictions by the main trading nations, many countries continue to pursue deeper trade integration and international cooperation. Even those countries implementing protectionist measures have continued to discuss, sign and implement “deep” trade agreements, i.e. those agreements that affect domestic policy areas beyond trade, such as the international flows of investment and labor, and the protection of intellectual property rights and the environment. Fortunately, the Deep Trade Agreements (DTA) database reports extensive information on the exact provisions included in each trade agreement signed up to 2023. As many as 61 percent of PTAs that were signed before 1995 covered fewer than 10 policy areas, while in the decade from 2013 to 2023, this figure dropped to just 8 percent.

For our purposes, following the spirit of Mattoo et al. (2022), we are interested in a specific aspect of the “depth” of a preferential trade agreement, namely the extent to which it regulates subsidies. Table 2 reports 12 questions that are included in the DTA database that can be used to construct a suggestive measure of the depth of an agreement with respect to subsidies.<sup>5</sup> . We define the variable _PTA_ _~~D~~ epth_ as a measure going from zero to (potentially) one, representing the share of affirmative responses to the 12 questions listed in table 2.<sup>6</sup> The median value of _PTA_ _~~D~~ epth_ is 0.16, and the range is from zero to 0.75. Interesting examples include the EU (0.58), CPTPP (0.33), USMCA (0.25), NAFTA (0.16), and RCEP (0.00).

# **2.2 Empirical Strategy**

The dependent variable is the growth rate of trade from origin country _i_ to destination country _j_ of
, providing some evidence of baseline comparability. In an alternate specification, we drop the control farmers from the neighboring villages and compare JCLIS beneficiaries only with their geographical neighbors while controlling for village fixed effects and find results similar to our main specification. Results from this alternate specification are reported in Table A.6 in the appendix. We also try to mitigate the selection bias in our study by using a Coarsened Matching and comparing only the matched sample of treated and control farmers. Results with the matched sample (Table A.7 in the appendix) are also qualitatively similar to the results from our main specification. Lastly, as a robustness check, we leverage remotely sensed data and control for two years of pre-treatment measures of Normalized Difference Vegetation Index (NDVI) for both groups of farmers (at a spatial resolution of 10 meters) in our main analysis. We find that our results remain the same, 7
Brazil, Thailand, and South Africa adopted such a regime, and several other countries (including those in East Asia that adopted a floating exchange rate following the 1997-98 financial crisis) are currently pondering the feasibility of such an approach.<sup>4 5</sup> Chile's experience is well documented by Landerretche, Morande and Schmidt-Hebbel (1999); accordingly, this section provides a brief discussion of the cases of Brazil, Thailand, and South Africa.

Table 2 summarizes the main characteristics of inflation targeting regimes in Chile, Brazil, South Africa, and Thailand. After the real crisis of January 1999 and the adoption of a floating exchange rate, Brazil adopted on June 21, 1999 an inflation targeting framework for monetary policy (see Bogdanski, Tombini, and Werlang (2000)). The inflation targets as well as the tolerance bands are set by the National Monetary Council, on the basis of a proposal by the Finance Minister. These targets must be set in principle two years in advance. The price index chosen for setting the inflation targets is the Broad Consumer Price Index (IPCA), reported by the National Bureau of Geography and Statistics (IBGE), although since March 2000 monthly measures of core inflation have also been published (see Figueiredo (2000)). The choice of a full index was dictated by the need to mitigate potential credibility problems associated with perceptions of deliberate manipulation of the index.

The operational framework of inflation targeting consists of a set of models to support the monetary policy decision process (including a small-scale structural econometric model, and short-term univariate forecasting models), supplemented with surveys of market expectations of major macroeconomic variables (including inflation and growth). The econometric model focuses on the aggregate demand channel of monetary policy, with a direct effect of changes in nominal interest rates, and an indirect effect through relative prices induced by the effect of changes in the nominal interest rate on the exchange rate.

The targets are considered to be met whenever the observed accumulated inflation during
and measurement of Ninis, in particular with regards to housework, family composition, schooling decisions, and the differential policy responses that these may point to (Pederzini, 2011; Rivero and Perderzini, 2014). This research calls for a better understanding of decisions to participate in schooling, the labor market, and housework.

The objective of this paper is to contribute to this body of literature by empirically analyzing youth decisions to attend school, work in the labor market, do both, or neither of these, and their association with past decisions regarding time use, as well as youths’ income, immutable endowments, socioeconomic context, and demographic characteristics, as suggested by the theoretical work of Behrman et al. (2014). Studying the dynamic decision process of Ninis is

> 6 This is the first attempt of analyzing the longitudinal nature of ENOE data to study the dynamics of youth decisions. In our paper we exploit further this characteristic of the data.

5
4 where CR is the variance of _R._ The estimator of 3, is equal to which, from eqn (1), shows that 61 is equal to _C._ Readers familiar with the RII will note that eqn (2) is essentially the same as the regression equation used to compute the RII, the difference being that the RII is typically computed using grouped rather than individual-level data. The division of the LHS through by p simply means that the coefficient 81 is the RII rather than the Slope Index of Inequality (SII) [18]. The only difference, then, between eqn (2) and the-equation used to compute the RuI is that the LHS contains the variance of the rank variable. This, however, approaches 1/12 as the sample size grows, and can therefore be treated approximately as a constant across samples. Thus the RII and C ought to rank distributions the same-there is little to choose between the two measurement approaches, though the concentration curve has the attraction of facilitating graphical comparisons of malnutrition inequalities.

# **STATISTICAL** INFERENCE

When undertaking cross-country (or temporal) comparisons, one needs to bear in mind that the malnutrition data are derived from survey data and are hence subject to sampling variation. It is useful, therefore, to couple numerical comparisons of the index C with statistical tests to assess the statistical significance of any inter-country (or temporal) differences. An attraction of the convenient regression-eqn (2) above-is that it provides a standard error for the concentration index C. This standard error is not, however, wholly accurate, since the observations in each regression equation are not independent of one another due to the nature of the R, variable. The following standard error estimator, derived by Kakwani et al. [15], takes into account the serial correlation in the data: where being the ordinate of _L(s),_ with qo = 0. It is this estimator that is used, rather than that in eqn (2)
Policy Research Working Paper 9198

# **Abstract**

While human mobility has important benefits for economic growth, it can generate negative externalities. This paper studies the effect of mobility on the spread of disease in a low-incidence setting when people do not internalize their risks to others. Using malaria as a case study and 15 billion mobile phone records across nine million SIM cards, this paper causally quantifies the relationship between travel and the spread of disease. The estimates indicate that an infected traveler contributes to 1.7 additional cases reported in the health facility at the traveler’s destination. This paper develops a simulation-based policy tool that uses mobile phone data to inform strategic targeting of travelers based on their origins and destinations. The simulations suggest that targeting informed by mobile phone data could reduce the caseload by 50 percent more than current strategies that rely only on previous incidence.

This paper is a product of the Development Impact Evaluation Group, Development Economics. It is part of a larger effort by the World Bank to provide open access to its research and make a contribution to development policy discussions around the world. Policy Research Working Papers are also posted on the Web at http://www.worldbank.org/prwp. The author may be contacted at smilusheva@worldbank.org.

_The Policy Research Working Paper Series disseminates the findings of work in progress to encourage the exchange of ideas about development issues. An objective of the series is to get the findings out quickly, even if the presentations are less than fully polished. The papers carry the names of the authors and should be cited accordingly. The findings, interpretations, and conclusions expressed in this paper are entirely those of the authors. They do not necessarily represent the views of the International Bank for Reconstruction and Development/World Bank and its affiliated organizations, or those of the Executive Directors of the World Bank or the governments they represent._ Produced by the Research Support Team
10.061|20.327|20.327|34.009|34.009|
|Observations|4,972|1,109|4,972|1,109|4,972|1,109|
|Equality test (Panel<br>vs. long differences)|p = 0|.000|p = 0|.000|p = 0|.016|
|<br>Equality test|||||||
|(country vs.|p = 0.000|p = 0.000|p = 0.000|p = 0.000|p = 0.000|p = 0.000|
|subnational)|||||||
|Number of countries|134|95|134|95|134|95|
|Number of regions|1,594|1,109|1,594|1,109|1,594|1,109|

_Notes:_ Robust standard errors in parentheses. Standard errors are clustered at the country level in Panel A and subnational level in Panel B. Regressions are weighted by region population. Poverty data are taken from the GSAP. Poverty and weather variables in the long-differences model are measured by the difference between averages of the earliest 3-year period and averages of the latest 3-year period. The long differences estimation is based on cross-sectional data with a smaller sample size compared with panel data. The data for the country-level analysis and the subnational-level analysis comes from our newly constructed database. The equality test p-values show the t-test between the panel FE results vs. the long differences results, and the country analysis results vs. the subnational analysis results. *** p<0.01, ** p<0.05, * p<0.1.

31
sources. Section 3 presents the empirical approach used to estimate the effects of childcare laws on FLFP. Section 4 presents the main results and is followed by robustness checks in Section 5. Section 6 concludes the paper.

# **2 Data**

Our analysis is based on data from 80 countries spanning a 20-year period (2000-2020). We combine information on childcare regulations, labor statistics, and socio-demographic indicators from the following sources to construct a country-year panel for our analysis.

# **2.1 Data sources**

_Childcare regulations._ The World Bank’s WBL database collects information on the legal environment pertaining to women’s economic participation and opportunity in 95 countries (Hyland et al., 2020). In particular, the dataset covers laws and regulations governing the supply and parental demand for childcare services. The dataset includes information on the dates of enactment and commencement of a law and other indicators related to accessibility, affordability, and quality of childcare services applicable to children below the formal pre-primary school starting age, as this is where the largest legal policy gap exists. The WBL database maps current legal and regulatory measures adopted by each country to ensure or strengthen the following three pillars of childcare (see list of indicators within each pillar in Table A1):

- Availability: The childcare law expands access to childcare by supporting different types of childcare provision and its convenience.

6
Vacancies published on portals are highly concentrated in the city of Pristina, which means that economic opportunities in other regions are not made publicly available on portals.<sup>32</sup> In Figure 38, Appendix C, we show a ranking of the 10 most frequent cities while accounting for the fact that a vacancy can indicate several potential cities for the job related to it (e.g., when firms have multiple sites in the country). If several cities are mentioned in a posting, Pristina is included most of the time. Other frequent job cities are: Prizren (197), Pejë (184), Fushë Kosovë (168), and Ferizaj (154). Postings on portals do not reflect well the economic dynamics in other regions (comparing with regional employment growth rates, see Figure 39, Appendix C). For example, the incidence of jobs in Pejë and Gjakovë is low despite strong economic activity due to the large retailers maintaining production sites there. One explanation is the type of clients for job portals, which are mostly medium to large firms highly concentrated in Pristina.<sup>33</sup>

# **VI. What are the skills needed by employers?**

In this section we present the main findings on skill demand based on job vacancy data in Kosovo. The purpose of this analysis is to explore which skills are sought after by employers who are hiring and to identify specific industry needs, at a granular level. Beyond skills, we are interested in firms’ requirements regarding education levels and previous work experience of potential employees. We use a skills taxonomy consisting of three layers, to provide as much detail as possible on the skills demanded by employers (Table 1). The general taxonomy presents three broad types of skills, namely cognitive, socio-emotional, and technical skills.

It is noteworthy that the literature on skills formation and investment in human capital interventions mainly highlights two types of skills: cognitive and non-cognitive skills (also sometimes referred as
public school, or decline it and apply to a private school or the outside sector.

> 17In principle, parents could play a further role by selecting between schools. Since our PISA data cannot speak to this issue, we leave the analysis of management-induced household selection for future work. Note that we assume management practices change effective labour inputs. In their study of the IT industry, Schivardi and Schmitz [2019] assume that management is an additional input, alongside capital and labour, in an approach that they term “management as a production technology”.

13
Figure 1: Estimates for Oil Production and Fiscal Revenues (2015-2070) Volumes of Production Oil Prices and Fiscal Revenues <!-- Start of picture text --> 180 10 120<br>160 9<br>8 100<br>140<br>7<br>120 80<br>6<br>100<br>5<br>60<br>80 4<br>60 3 40<br>2<br>40<br>1 20<br>20<br>0<br>02020 2025 2030 2035 2040 2045 2050 2055 2060 2065 2070 2075 -1 2020 2025 2030 2035 2040 2045 2050 2055 2060 2065 2070 2075 0<br>Oil, MBBL/year Gas, MBBoe/year Fiscal Revenue Oil Prices (RHS)<br>USD<br>Billion USD<br>MBBoe/year<br><!-- End of picture text --> and public investment are set at 16 and 8.6 percent of GDP, respectively, and private investment is set at 13.7 percent of GDP. We choose the shares of traded goods to be 60 percent in private consumption and 40 percent in government purchases, as government consumption typically have a larger component of nontraded goods than private consumption. The share of natural resources is 40 percent of GDP at the initial steady state.

- _Assets, debt and grants_ . Government savings are 0 percent of GDP ( _RFshare_ = 0) at the initial steady state. For government domestic debt, concessional debt, grants, as well as private foreign debt and government external commercial debt we rely on World Bank data. This implies _bshare_ = 0 _._ 268, _dshare_ = 0 _._ 114, _grshare_ = 0 _._ 01, _b_<sup>_∗_</sup> _share_<sup>= 0and</sup><sup>_dc,share_= 0</sup><sup>_.
arose from supply side effects; the voucher had credential value to workers – it acted like a “letter of introduction” that few people had (and how it was allocated was a secret locally). This could not be revealed by the (randomized) evaluation, but required supplementary data. The extra insight obtained about how _Proempleo_ actually worked in the context of its trial setting also carried implications for scaling up, which put emphasis on providing better information for poor workers about how to get a job rather than providing wage subsidies.

Spillover effects also point to the importance of a deeper understanding of how a program operates. Indirect (or “second-round”) impacts on non-participants are common. A workfare program may lead to higher earnings for non-participants. Or a road improvement project in one area might improve accessibility elsewhere. Depending on how important these indirect effects are thought to be in the specific application, the “program” may need to be redefined to embrace the spillover effects. Or one might need to combine the type of evaluation discussed here with other tools, such as a model of the labor market to pick up other benefits.

The extreme form of a spillover effect is an economy-wide program. The evaluation tools discussed in this chapter are for assigned programs, but have little obvious role for economy-wide programs in which no explicit assignment process is evident, or if it is, the spillover effects are likely to be pervasive. When some countries get the economy-wide program but some do not, cross-country comparative work (such as growth regressions) can reveal impacts. That identification task is often difficult, because there are typically latent factors at country level that simultaneously influence outcomes and whether a country adopts the policy in question. And even when the identification strategy is accepted, carrying the generalized lessons from cross-country regressions to inform policy-making in any one country can be highly problematic. There are also a number of promising examples of how simulation tools for economy wide policies such as Computable General Equilibrium models can be combined with household-level survey data to
’s level of education is not above the education level|
|D. Linkage between education and job|Low incidence of overqualifcation|Cell-based<br>threshold|of the median worker in the sector-occupation-birth cohort-<br>graduation cohort cell.|

_Notes:_ Based on data from EH 2007-2021. We define workers in micro and small enterprises (MSE) for people who are employed in a firm with less than five employees, domestic workers and unpaid workers. Average income is in 2010 prices.
where _LGDi_<sup>_TTC_</sup> is the through-the-cycle value of LGDs taken from the QIS5 study, _PDi_<sup>S</sup> , _j_ are the values computed in (9), and the parameter _ρLGD_ , _PD_ controls the extent of the correlation between LGDs and PDs. Note that there exists considerable evidence in the literature to suggest that _ρLGD_ , _PD_ increases during crisis times, that is, under the Stress scenario (see, for instance, Altman _et al._ , 2002). We therefore suggest to calibrate the _ρLGD_ , _PD_ parameter in the 10% _−_ 20% range for the non-crisis TTC and PIT scenarios, and increase the _ρLGD_ , _PD_ parameter to the 30% _−_ 50% range during crisis times (under the Stress scenario). The latter range corresponds to the preliminary results found in the study by Moody’s (2010) for the global financial crisis period.

Notice from (10) above, that because the _PDi_<sup>S</sup> , _j_<sup>are bounded by [</sup><sup>_PD_</sup> _i_<sup>S,</sup><sup>_PD_</sup> _i_<sup>S+</sup><sup>_κ_], where</sup> _κ PD_<sup>S</sup><sup>_PD_S</sup><sup>_PD_S</sup> will be greater than 0, the term ( _i_ , _j_<sup>/</sup><sup>_PD_</sup> _i_<sup>S</sup><sup>_−_1) will be 0 if</sup> _i_ , _j_<sup>=</sup> _i_<sup>and it will be</sup> equal to _κ_ / _PDi_<sup>Sif</sup><sup>_PD_</sup> _i_
21 Unfortunately, the ECH data do not follow households over time. Therefore, we cannot implement our difference-indifference methodology at the household level.

22 We also run these regressions for households above and below the poverty line. The results, which are available upon request, suggest that nationalization increased the network access rate of poor households.

23 The World Bank provided Uruguay with financial support to develop water and sanitation services through three investment loans at different time periods: 1) Water Supply Rehabilitation project (1988–1999), USD 22.3 million; 2) OSE Modernization and Systems Rehabilitation project, APL-1 (2000–2007), USD 27 million; 3) OSE Modernization and Systems Rehabilitation project, APL-2 (ongoing since 2007), USD 50 million. Other loans focused on technical support, such as the Public Services Modernization Technical Assistance project (2001–2008) for USD six million. The objective of these loans was to help Uruguay make investments in the water infrastructure, improving the efficiency and coverage of the water supply and sanitation services. See The World Bank (2010) for additional details.

24 When testing the negative binomial estimator, the Vuong test takes the value 5.79 in the specification without controls and 6.45 in the specification with controls. When testing the Poisson estimator, the Vuong test takes the value 1.81 without controls and the value 1.87 with controls. They all reject that the ordinary Poisson or negative binomial models should be preferred to the zero-inflated estimators at the 5 percent level, which is unsurprising given the large number of zeroes.

25 Note that when using a public provider dummy that takes a value of one when the city is served by a publicly owned water company in lieu of a nationalization dummy, we obtain qualitatively and quantitatively similar results to those reported in table 7.

35
and the data disclose the date and time of each transaction, a stock identifier, order type (buy or sell), transaction price, number of shares, broker, and investor type (i.e., individuals or institutions). Transaction costs such as broker fees are not captured by the CSE.<sup>18</sup> During our sample period, trades by individual investors accounted for over seven million transactions. Importantly for our analysis, the data include the national identification number for each individual, which can be used to merge the information with the financial education initiative. Overall, 36% of the students in the CSE’s education program had at least one stock trade throughout our sample period, either before or after taking the course. These 3,960 students with some trading history are more active in the stock market than the average Colombian individual investor. For example, while the average investor in Colombia owns 2.2 different stocks, makes 1.3 stock transactions per year, and averages 7,200 USD per trade, the program students who actively trade hold 6.2 stocks, make 6.6 trades per year, and average 8,781 USD per trade.

Third, we collect additional information about students via an electronic survey. The survey had three parts: (i) socioeconomic information (i.e., age, education, academic history, and earnings); (ii) experience in financial assets (e.g., whether the individual had any foreign investments or mutual funds, which are not captured in the CSE data); and (iii) self-reported social interaction (i.e., whether they took the course with a friend or a relative and whether they talked to classmates about investment strategies during the course). The survey was sent electronically in March 2018 to 4,600 students with emails reported in the CSE data set.<sup>19</sup> To encourage participation, students who completed the