rafmacalaba/datause-encoder-data huggingface dataset:
<!-- Start of picture text --> o Project Preparation Costs in IDA-only Portfolio<br>Ss<br>8<br>o<br>o<br>s3<br>S<br>7+<br>N<br>:ooO |<br>2001 2002 2003 2004 2005 2006 2007 2008 2009 2010 2011 2012 2013<br>Approval FY<br>HE NeverFCS [EN Aways+Partial FCS<br><!-- End of picture text -->

<!-- Start of picture text --> o Supervision Costs in IDA-only Portfolio - BB + TF<br>S<br>°<br>2<br>8<br>g8<br>e<br>wo<br>oO<br>2001 2002 2003 2004 2005 2006 edi 2008 2009 2010 2011 2012 2013<br>GE sNeverFCS [EN Always+Partial FCS<br>TFIEG data 2013included Report FCSwhere countryavailablecategorizationfrom BW. adopted adding South Sudan;<br><!-- End of picture text --> <!-- Start of picture text --> Relative Preparation Costs in IDA-only Portfolio<br>8<br>= oOo<br>f=<br>°<br>8<br>oy<br>ot<br>e<br>|aoo il i<br>2001 2002 2003 2004 2005 2006 2007 2008 2009 2010 2011 2012 2013<br>Approval FY<br>HE NeverFCS [EN Aways+Partial FCS<br><!-- End of picture text -->

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Since the education system is mostly public at all levels of education in Croatia and there is no evidence of wide-spread opting-out of public education, benefits were assigned to households according to the number of children attending each level of education, according to the cost-perpupil of delivering education service at each level. For health services, all individuals have access to services, so we use the cost of insurance approach and assign a per capita benefit equally to all individuals. This is to ensure that sicker individuals are seen as being “better off”, simply because they use public services more often.

There are some important caveats about what the fiscal incidence analysis applied here does not address. First, it does not take into account behavioral, lifecycle or general equilibrium effects and focuses on average incidence rather than incidence at the margin. Our tax shifting and labor supply response assumptions are strong because they imply that that consumers have perfectly inelastic demand and that labor supply is perfectly inelastic too. Second, the analysis does not take into account the intra-household distribution of consumption. Third, the analysis cannot take into account the quality of services delivered by the government. In addition, we are unable to include some important taxes and spending. Corporate profit taxes, VAT paid by government or institutional consumption, and spending on infrastructure investments are excluded, even though the impacts of these may be substantial simply because the methods to assign these taxes and transfers are not robust. Finally, the analysis does not capture the growing debate on how asset accumulation and returns to capital impacts income inequality.

# **IV. Impact of taxes and social spending on poverty and inequality**

## **The impact on inequality**

The combined effect of taxes and social spending help to substantially reduce inequality in Croatia. Figure 2 shows the change in the Gini coefficient on account of taxes and social spending following the income concepts defined above for 2014. Prior to any fiscal intervention, market income inequality had a Gini of 0.383, if old-age contributory pensions are included and are treated as deferred income, but much higher (0.513) if these pensions are not included. If pensions in Pillar I are treated as <!-- Start of picture text --> Figure 2. Croatia. Gini Coefficient<br>0.55<br>Old-age pensions as deferred<br>0.50 income<br>Old-age pensions as transfers<br>0.45<br>Old-age Pillar I as transfers;<br>0.40 Pillar II as deferred income<br>0.35<br>0.30<br>0.25<br>Market Income Disposable Consumable Final Income<br>Income Income<br><!-- End of picture text --> <u>Source: Own estimates using the Croatian PIS and HBS 2014.</u> 13
2 competitive market, but facing default risk (see for example Eaton and Gersovitz (1981), Cooper and Sachs (1985), Sachs and Cohen(1985)).

An attempt to determine whether repeated experience of a country had an affect on the behaviour of credit terms requires detailed information on the loan contracts signed by the countries. The data set employed contains information on commercial bank loans from Euroci.rrency credit markets. Three aspects of this data set are important to note. First, since the rates contracted in early stages of borrowing may differ from those contracted later, we incorporate information on early loan contracts.3 Second, since we are concerned with the evolution of the market, and we want to avoid problems surrounding the debt crisis we limit the data to a period before the general debt crisis of 1982. Third, we do not include information on renegotiated debt. This is because we are concerned with the operation of the Eurocurrency market for countries that have not experienced repayment difficulties on their commercial bank loans. Accordingly, we assembled a loan level data for 55 countries during 1968 to mid-1981 (2148 bank loans). The data include information on the terms of the loan contracts signed, as well as the month and year of the loan agreeement. A measure of repeated experience is simply defined as the cumulative number of months in which the borrower received a loan (other definitions are also employed as will be presented later in the paper). The experience variable is distinct from a cumulative number of months over calender time since the countries in the sample have different entry dates (Chile in October 1968, Zimbabwe November 1980).

The primary finding is that the experience level of a borrower contributes significantly to the determination of the spreads. Specifically, the spreads are shown to start at rates that are higher than would be
# **Bring a Friend: Strengthening Women’s Social Networks and Reproductive Autonomy in India**<sup>∗</sup>

S Anukriti Catalina Herrera-Almanza Mahesh Karra The World Bank<sup>†</sup> University of Illinois at UrbanaBoston University<sup>§</sup> Champaign<sup>‡</sup> _Keywords:_ Women’s Social Networks; Peers; Vouchers; India; Family Planning; Contraception; Female Autonomy; Mother-in-law _JEL codes:_ J13, J16, O15, O33, I15, Z13.

> ∗This study was registered in the AEA RCT Registry (AEARCTR-0003283). We are grateful to Praveen K. Pathak for overseeing the fieldwork in his capacity as the local Principal Investigator. We thank Rocio Valdebenito and Esther Lee for research assistance, and Sonam Bhadouria and Pratibha Tomar for fieldwork, management, and oversight of the study in Jaunpur. We also thank Kathleen Beegle, Tanya Byker, David Canning, Peter Christensen, Andrea Flores, Andrew Foster, Matt Jackson, Seema Jayachandran, Annemie Maertens, Hope Michelson, Mushfiq Mobarak, Emily Oster, Berk Özler, Silvia Prina, Carolina SanchezParamo, Anja Sautmann, Rebecca Thornton, and various seminar and conference participants at ASSA Meetings, Ashoka University, Boston University, BREAD Conference at Stanford, Delhi School of Economics, IFPRI, IIM Bangalore, ISI Delhi Conference, Midwest Development Day, NEUDC, OARES-Minnesota, PAA Meetings, Population Health Science Research Workshop, Universidad del Rosario, Advances with Field Experiments Conference at University of Chicago, and World Bank for helpful comments. This project was supported by a Northeastern University Tier-1 Grant and funding from the Human Capital Initiative’s Program for Women’s Empowerment Research at the Boston University Global Development Policy Center. Ethical approval for the trial was granted by the Northeastern University Institutional Review Board and in India by the University of Delhi Research Council. The findings, interpretations, and conclusions expressed in this paper are entirely those of the authors. They do not necessarily represent the views of the World Bank and its affiliated organizations, or those of the Executive Directors of the World Bank or the governments they represent.

> †Development Research Group. `sanukriti@worldbank.org` .

> ‡Department of Agricultural and Consumer Economics. `cataher@illinois.edu.`

- §Frederick S. Pardee School of Global Studies. `mvkarra@bu.edu.`
business practices predict higher survival rates and faster sales growth (McKenzie and Woodruff, 2017).

Several channels have been suggested to explain how management and organizational practices lead to higher productivity. Workforce management through supervision and the setting of appropriate incentives can encourage higher levels of effort from workers. For example, basing pay on output instead of paying a fixed rate by the hour increased productivity by 44 percent in a windshield installation firm in the US (Lazaer 2002). In cases where output is not directly measurable or contractable, good workforce management can still increase effort: Baker, Gibbons & Murphy (2002) and Gibbons & Henderson (2013) argue that good managers are able to establish relationship of trusts and that through these “relational contracts” with their workers, employees exert higher levels of effort knowing that this will be reciprocated in some form in the future. Nevertheless, setting incentives can be tricky. Badly-aligned incentives can create a barrier to firms adopting more productive technologies, as Atkin et al. (2017) showed with soccer-ball producers in Pakistan, or can undermine worker moral, as Breza, Kaur & Shamdasani (2018) showed with performance pay in a manufacturing plant in India.

Other channels contributing to higher levels of productivity include careful monitoring, measurement and recording of performance output, allowing for rapid adjustments if necessary (e.g., continuous improvement). Better recruitment and training practices can lead to both better hires as well as increased interaction and better problem-solving by employees (Gant, Ichniowski, and Shaw, 2002; Ichniowski, and Shaw, 2009).

# _Skills_

Human capital is a key driver of productivity in both low- and high-income countries. Growth accounting decompositions and comparisons of regional income data suggest that about half of the differences in income levels between countries can be explained by education (Acemoglu & Dell 2010, Mankiw, Romer & Weil 1992).

The skills that workers bring to the firm influence productivity and performance. Gennioli et al. (2013) use Enterprise Survey data of 20 countries – including Mozambique – to estimate that an additional year of education of an employee leads to a 6-7 percent return in productivity. Moretti (2004) finds that American firms in cities with more college-educated inhabitants are more 5
Policy Research Working Paper 10868

# **Abstract**

Are changes in the labor market in response to changes in exports contained specifically within exporting industries, or do they disperse throughout the economy through supply chain linkages? This paper studies the case of Viet Nam, an example of a successful export-led growth economy, to examine this question. Combining UN COMTRADE data, input-output tables from the Global Trade Analysis Project, and 2010 to 2019 annual labor force survey data for Viet Nam, the study constructed a measure of each worker’s total exposure to export shocks. The measure accounts for changes due to both direct export exposure (increase in exports in the worker’s own industry) and indirect exposure (from increased exports in other industries that use inputs from the worker’s industry). Estimates of the repercussions from increasing exports on labor market outcomes show that both direct and indirect exposure significantly increase workers’ wages and employment, while reducing inactivity and inequality. Wage premiums for attending college decrease, and the gender wage gap narrows. Wages increase more for the lowest-income workers and employment gains accrue more to unskilled workers, while employment decreases for more skilled workers.

This paper is a product of the Poverty and Equity Global Practice. 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 authors may be contacted at gacevedo@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
outcomes across genders, or alternatively, the differentiated effect of gender across ethnicities.<sup>28</sup> As explained by Puhani (2008) in a discussion on non-linear difference-in-differences model, β3 will have the same sign as the “treatment effect” since f(⋅) is strictly monotonic.

As a virtue of the similarity to difference-in-differences, we are able to compare the difference in educational attainment for each sub-group with respect to males in the reference group as follows:

- i. The marginal effect of the first term (Gi) shows the difference in education outcomes between _females in the reference group_ ( Gi = 1 ∧ ETHi = 0 ) and males in the reference group (Gi = 0 ∧ ETHi = 0).

- ii. The marginal effect of the second term (ETHi) shows the difference in education outcomes between _males in the minority group_ (Gi = 0 ∧ ETHi = 1) and males in the reference group (Gi = 0 ∧ ETHi = 0).

- iii. The sum of the marginal effects for each of the three terms (Gi, ETHi, Gi ∗ETHi) shows the difference in educational attainment between _females in the minority group_ (Gi ∗ETHi = 1 ⇔Gi = 1 ∧ ETHi = 1) and males in the reference group (Gi = 0 ∧ ETHi = 0).

# **4. Results**

Before discussing the findings of the multivariate analysis, it is illustrative to highlight literacy and educational attainment in the reference and minority groups (Table 2). In three of the four countries for which literacy data are available (Bolivia, Mexico and Peru), the literacy rate is the highest for males in the reference group, followed by females in the reference group, and then by ethnic minority males and ethnic minority females, respectively. The only exception is Sierra Leone, where gender-based disparities in literacy are greater than ethnicity-based

> 28 Sen, Iyer and Mukherjee (2009) argue that analyses that take the product of two dummy variables in a regression model cannot compare outcome disparities across sub-groups with intersecting identities. This is because the interaction terms capture differences between one reference pair (e.g. ethnic minority women) with respect to all other pairs taken together. This is not the case in this model, where the binary nature of the explanatory variables allows us to infer the distinct impacts on all four groups under consideration.

15
# **Fig. 25. Unemployment and activity rates**

<!-- Start of picture text --> Registered unemployment rate<br>Unemployment rate by LFS<br>% Activity rate (right-hand scale) %<br>25 57<br>56<br>20<br>55<br>15<br>54<br>10<br>53<br>5<br>52<br>0 51<br>2002 2003 2004 2005 2006 2007 2008 2009 2010 2011<br>Source: CSO.<br><!-- End of picture text --> 34
non-financial data – and they also review public information (produced by the company or by other sources of information). Such direct, full assessment of the ESG practices of each counterpart is not always feasible given the large amount of information to be processed and the large number of firms. The spectrum of firms covered by the main rating agencies is very wide, as each agency may cover from 1,000 to 10,000 companies (Moody’s, 2021). However, the coverage of smaller companies, less regulated industries and emerging countries by the main rating agencies is particularly patchy.

To address the data constraints issues related to SMEs, Moody’s is complementing its firm-level approaches with another methodology, namely with a “deductive” top-down approach (“ESG Score Predictor”). It developed a model of ESG ratings for a set of companies whose metrics are known, which it then applies to “unscored” companies (Moody’s, 2021). By this method, it enlarges the spectrum of companies with an ESG rating (potentially to an infinite number). It does so by leveraging on only 3 firm-level dimensions: company size (based on assets or turnover), location and industry (615 NACE4 industries). The firm-level information (size, location and industry) is complemented with numerous country level data (more than 50 criteria) on socioeconomic indicators and scores. If size, location and industry are known, the model leverages on these three inputs to generate an ESG rating for any company. Hence the Moody’s ESG Score Predictor methodology, which is very partial (with less information available, it generates an output covering by far more firms compared to “traditional” ESG ratings), does not rely on any firmlevel ESG-related information, as the full assessment of each company is not possible. This is different from our methodology, which is entirely based on firm-level information of the Enterprise Survey and does not leverage any country data, despite the availability of ESG country data for numerous emerging countries (World Bank, 2021).

The rating methodologies provided by the main ESG rating agencies differ from each other significantly (in terms of building blocks, materiality assessment, weights used to aggregate the information, etc.) and cannot be reconciled under one single approach. The main building blocks utilized by the ESG rating agencies are typically the following (sometimes similar concepts appear under different names):

- Environmental:

- Environmental strategy or green management

- Environmental impact or footprint

- Social

- Gender

- Human resources, including education and training

- Labor conditions

- Community involvement

- Governance

- Corporate governance and stakeholders

with oekom research in 2018; Sustainalytics, which is part of Morningstar; MSCI ESG Rating (which in 2019 acquired Carbon Delta, a Zurich-based environmental fintech and data analytics firm specializing in climate change scenario analysis).

4
percent and 0.16 percent, respectively. By contrast, purchases of dollars are generally not statistically different from zero, suggesting that sterilized interventions of this nature do not influence the exchange rate mean level.

**Table 5. EGARCH(1,1) Estimations: Central Bank dollar sales/purchases in** **<u>amounts and frequencies.</u>**

|Mex|ico|Tu|rkey|
|---|---|---|---|
|Magnitudes<sup>_a_</sup>|Frequencies|Magnitudes<sup>a</sup>|Frequencies|
|_Mean Equation_||||
|φο<br>9.5e-09|4.8e-09|0.00033|0.00051|
|(0.0003)<sup>_b_</sup>|(0.0010)|(1.2560)|(1.4792)|
|φ_sales_<br>0.00009<sup>*</sup>|0.01279<sup>*</sup>|0.00002<sup>*</sup>|0.00158<sup>*</sup>|
|(9.0397)|(8.5266)|(3.2444)|(2.7893)|
|φ_urchs_<br>4.8e-07|6.6e-05|0.00003|0.00001|
|_p_<br>(0.5829)|(0.6227)|(1.4543)|(0.0049)|
|φ_sign_<br>-0.00039|0.00009|0.00209|0.00215|
|(-1.0345)<br>|(0.2464)<br>|(1.2576)|(1.5071)|
|φ_brady_<br>-0.00071<sup>*</sup>|-0.00078<sup>*</sup>|_|_|
|(-5.0664)|(5.5539)|||
|_Variance Equation_||||
|<br>ω<br>-1.6150<sup>*</sup>|-1.8440<sup>*</sup>|-2.7575<sup>*</sup>|-3.4023<sup>*</sup>|
|(-5.2121)|(-5.4996)|(-2.9329)|(-2.9118)|
|α<br>0.14480<sup>*</sup>|0.14820<sup>*</sup>|0.4706<sup>*</sup>|0.5889<sup>*</sup>|
|(3.1443)|(3.0129)|(2.7800)|(3.4734)|
|<br>β<br>0.8797<sup>*</sup>|<br>0.8661<sup>*</sup>|<br>0.7708<sup>*</sup>|<br>0.7193<sup>*</sup>|
|<br>(36.5108)<br>|(34.0331)<br>|(8.8436)|(6.6930)|
|γ<br>-1.0000<sup>**</sup>|-1.0000<sup>**</sup>|||
|<br>(-12.3337)|(-2.3134)|_|_|
|<br>δ_sales_<br>-0.01091<sup>*</sup>|<br>-2.1930<sup>*</sup>|-0.00308<sup>**</sup>|-0.3419<sup>***</sup>|
|(-5.5154)<br>|(-4.9335)|(-2.2545)|(-1.7891)|
|δ_purchs_<br>0.00153<sup>***</sup>|0.03651|-0.00169|-0.1116|
|(1.7611)|(0.3482)|(-0.2805)|(-0.3771)|
|<br>δ_ON_<br>_c_<br>0.00023<sup>*</sup>|<br>0.00025<sup>*</sup>|<br>-0.00167<sup>*</sup>|<br>-0.0018<sup>*</sup>|
|<br>(2.7190)|(2.6158)|(-4.0173)|(-3.7717)|
|δ_si_<br>-0.03104|0.1347|0.17198|-0.0405|
|_g_<br>(-0.0895)<br>|(0.3526)|(0.1657)|(-0.03897)|
|δ_put_<br>-0.00026<sup>**</sup><br>(-1.9518)|_|_|_|
|_Decision Criteria_||||
|AIC<sup>**_d_**</sup><br>-12,556.8|-12,578.2|-2,316.8|-2,310.6|
|<br>_BIC_<sup>_d_</sup><br>-12,474.3|<br>-12,500.9|<br>-2,275.5|<br>-2,269.2|
|<br>_Q_ε_(20)_<sup>_e_</sup><br>23.99|<br>24.52|<br>15.56|<br>15.75|
|[0.2428]<sup>f</sup>|[0.2204]|[0.7435]|[0.7320]|
|_Q_<sup>_2_</sup><br>ε<sup>_(20)e_</sup><br>4.61|13.37|14.60|28.87|
|<br>[0.9998]|[0.8610]|[0.7988]|[0.0904]|

*, ** and *** denote significance at the 1, 5 and 10% levels respectively.<sup>a</sup> In millions of US dollars;<sup>b</sup> _t-ratios_ in parenthesis;<sup>c</sup> ON denotes the target for cumulative balances (short) in Mexico and the overnight interest rate in Turkey;<sup>d</sup> AIC and BIC are the Akaike and Bayes Information Criteria respectively;<sup>e</sup> Qε(20) and Q<sup>2</sup> ε<sup>(20) are the twentieth-order Ljung-Box tests for correlation in the</sup> residuals and in the squares of the residuals;<sup>f</sup> P-values in brackets.

21