rafmacalaba/data-use-mentions-extended huggingface dataset:
domestic or foreign sourced, account for about 37 percent of the value of manufacturing exports. This number increases substantially to 53 percent when adding service activities that take place within manufacturing firms. Facilitating provision of services and thereby increasing GVC participation through backward linkages enables firms, particularly downstream manufacturing firms, to access a greater variety and quality of services at competitive prices, which in turn improves their participation in GVCs and overall productivity.

Hence, the role of services as input into production is substantial. They are the engine for many firms to integrate into GVCs. Transport, logistics, ICT services, marketing, consultancy and professional services play an important coordinating role in GVCs. Empirical evidence suggests that effective provision of services is important for downstream firm performance, including in India (Arnold et al., 2015; Arnold et al., 2011) and that imported services contribute to the export intensity of manufacturing in India (Goldar et.al 2018). Effective provision of high-quality services through removing policy barriers related to supply of services; strengthening the institutional framework and investing in human capital will support the integration of India and other developing countries into GVCs and help them reap the associated economic benefits of specialization, scale and productivity growth.

This paper explores the relationship between the use of service inputs and GVC participation and contributes to the emerging empirical literature in this area. India provides an interesting setting to study this as a fast-emerging economy where the services sector plays an important role. We document the extent of integration of India in global value chains using both macro and micro firm-level data (for the 2
per 100,000 inhabitants. In four of the five specifications included in Table 2, higher dropout rates are positively associated with our measure of violence. This result is consistent with a vast literature, mainly from the United States, showing that time spent in school is negatively associated with crime rates. For instance, using county-level data for the United States during the period 1980-2008, Anderson (2014) shows that the minimum dropout age is negatively related to juvenile crime. Employing a regression discontinuity exploiting the exogenously determined _cut day_ for enrolling, Cook and Kang (2014) find that members of the delayed entry group are more likely to drop out and to be convicted of a serious crime. High school dropout rates decreased 2.3 percentage points during the period of violence reduction in Mexico (1995-2007), contributing a decline 20
additional year of schooling increases income per worker by approximately 18.4 percent.<sup>27</sup> This number suggests that the macro return to schooling in Guatemala is rather high, but it compares favorably with earlier microeconomic evidence. For example, the World Bank (1995) reports a private return to schooling of 14.9 percent for Guatemala.<sup>28</sup> There is evidence for much lower returns in the informal sectors and for decreasing patterns over time, but the magnitude of the coefficient is echoed in Funkhouser (1997). An estimate from Haeussler (1993) based on 1989 survey and Ministry of Education data suggests that, depending on the schooling level and underlying assumptions, the social return to schooling lies in a band between 13-19 percent. Finally, these results also confirm the cross-country evidence from Cohen and Soto (2001). They essentially find that in macro and micro regressions the effect of education on income is of similar magnitude.

# **6. Robustness Check and Additional Explanatory Variables**

This section seeks to answer some basic questions. How much confidence should be placed on the previous results? Evidently, given certain data restrictions and distortions caused by the civil war, a key issue is if the previous findings can be used to derive firm policy conclusions. In addition, another important aspect is considered: does the conditioning information set cause the schooling coefficients to change?

In order to answer these questions, the following paragraphs are organized as follows. Section 6.1 tests the stability of the variables. By comparing the results with alternative sources, section 6.2 includes time trends, and analyzes the reliability of the human and physical capital stock data. Section 6.3, the bulk of the analysis, includes additional variables explaining growth. An overview of the alternative data is presented in Figure 7. Additional variables are the quality of capital, trade openness, terms of trade, and imported capital goods. This section also examines the effect of
friends and neighbors are obvious groups for both social comparisons and as generators of positive externalities such as through risk-sharing, there may be other groups that matter. Finding that people do not feel RD with respect to their friends and neighbors does not

> 19 For supportive evidence see Ravallion and Dearden (1988) (using data for Java, Indonesia).

> 20 Surveys of the lessons for economics from the “new psychology of happiness” can be found in Frey and Stutzer (2002) and Layard (2005).

13
Table A.9: Impact of expected dowry on fertility and sex ratio, REDS data

||(1)|(2)|(3)|(4)|(5)|
|---|---|---|---|---|---|
|**Dependent variable:**||**Numb**|**er of bir**|**ths**||
|_Firstborn girl_ * _Expected dowry_||0.085**|0.054*|0.055|0.044|
|||[0.031]|[0.029]|[0.042]|[0.045]|
|_Firstborn girl_|0.302***|0.080|0.123|0.119|0.132|
||[0.060]|[0.126]|[0.124]|[0.161]|[0.156]|
|_Expected dowry_||-0.060|-0.022|||
|||[0.041]|[0.074]|||
|N|3,078|3,078|3,078|3,078|2,846|
|Dep var mean for Firstborn boy|2.285|2.285|2.285|2.285|2.285|
|**Dependent variable:**|**Fr**|**action s**|**ons (par**|**ity** _≥_**2)**||
|_Firstborn girl_ * _Expected dowry_||-0.004|-0.007|-0.008|-0.011|
|||[0.010]|[0.015]|[0
obtaining a secondary education or above. Furthermore, children in more well educated households (parents who have secondary education or above) have a greater chance of obtaining a higher education than the children of parents who have no education or who have completed only primary education. Thus, children with poor parental education still have a lower prospect of attaining higher education. The overall results suggest that all the counties experienced upward intergenerational educational mobility over the last 50 years. However, the observed mobility in almost all the countries is concentrated in the lower tail of the education distribution, primary education. It is plausible that this is the result of the expansion in primary education in the region after independence in the 1960s. In line with our results in previous sections, we document evidence that maternal education is more important in influencing children’s education relative to schooling of fathers in all the countries but the Comoros.

# **6 Conclusion**

Drawing on nationally representative survey data, we study the intergenerational im(mobility) of educational attainment in the Comoros, Ghana, Guinea, Madagascar, Malawi, Nigeria, Rwanda, Tanzania, and Uganda over 50 years, with a particular focus on gender differences. The overall results indicate that there has been a significant improvement in intergenerational educational mobility during the last five decades, particularly after the 1960s. We document a country difference: Nigeria, Guinea, Ghana, and Uganda experienced the highest intergenerational mobility, and the Comoros and Madagascar the lowest. Nevertheless, the educational attainment of parents remains a strong determinant of children’s schooling outcomes. We also find considerable gender differences in the persistence of education across generations, which are masked in the country estimations; the educational attainment of daughters is more closely correlated with parental years of schooling relative to educational attainment of sons. On paternal or maternal effects, the education of mothers is significantly more important than the education of fathers in shaping the educational attainment of both daughters and sons, though the effect is much stronger among daughters. Furthermore, we
(ii) assess the impact of greening financial portfolios in terms of the composition of the real economy, prices and distributive effects; (iii) assign risk weights to assets, depending on whether they are high or lowcarbon; and (iv) calculate the investment risk profile in a forward-looking way, across climate mitigation scenarios (e.g. the NGFS ones), thus affecting the eligibility criteria.

# **3.3.2 Enabling conditions for the implementation of GFSI**

We can identify the following set of general enabling conditions that would foster the effectiveness of all GFSI.

> 31 The CPRS classification can be downloaded at: https://www.finexus.uzh.ch/en/projects/CPRS.html

> 32 These include the European Banking Authority, The European Insurance and Occupational Pension Authority, the European Securities and Markets Authority, the European Central Bank, the Swiss National Bank and the Austrian National Bank. In the context of EMDEs, CPRS have been already applied by the Caribbean Development Bank to its project portfolio in the Caribbean region, by Monasterolo et al. (2018) to the energy infrastructure projects of the China Development Bank and Import-Export Bank of China outside the Chinese borders, and by Roncoroni et al. (2021) in the climate stress test of Mexico.

20
contrast, only 20 % of men travel on foot, and more than 50 % of men use some transportation. There is also evidence that women use slower modes of transportation to commute to work as faster modes are usually more expensive (Anand and Tiwari, 2006). Concurrently, female labor force participation is low in India, at a rate of 28.2 % of the working-age population as of 2019, significantly lower than the average of 46 % in low- and middle-income countries.<sup>23</sup> We leverage the _Pink Slip programs’_ state-wide roll-out in Punjab and Tamil Nadu in April and May 2021, respectively. While Delhi also introduced this program in November 2019, data limitations preclude us from including it in our sample.<sup>24</sup> Several media

> 22India’s 2019 National Time-use Survey (https://mospi.gov.in/time-use-survey).

> 23World Bank’s Gender Data portal (https://tinyurl.com/4a8rm5ye). According to the most recent female labor force survey by India’s Ministry of Statistics and Program Implementation, it increased to 37% (http://tinyurl.com/mu2vx7m3).

> 24The CMIE, our data source in this paper, started collecting information on individual time usage after Delhi started implementing the _Pink Slip Program_ . So, we are unable to include Delhi in our sample.

1
city. In terms of comparison, the Mexican survey drew on the city with highest per capita earnings, while the two cities surveyed in the U.S. were considered below average for that country.

Table 12 presents an overview of the socioeconomic characteristics of the survey population. As remarked in the introduction, income per capita in Mexico is about one fourth what it is in the United States. Indeed, the surveys show less than 45% of the U.S. households with annual income below US$15,000 compared with close to 80% in Mexico City. As noted in the table, in Mexico City 23.6 percent of the population reported that they had savings in a bank (21.3% had savings but did not borrow from a bank and 1.8% had savings and credit from a bank). As expected, this implies a much higher percentage of the population in Mexico is unbanked compared to the U.S. While noteworthy, the differences in household income across the two countries do not appear to account for the differences in the coverage of banking services.

> 15 Estimated data of  GDP by SIREM March 12, 2004

37
et al., 2020). Then the 2020 population has been predicted further to 2022 as shown in Column 1 using strata-level population growth obtained from the Committee of the Republic of Armenia (ArmStat). The estimated total population in 2022 in this data is 2.9 million, close to the official statistics from ArmStat. Column 2 displays the 2023 number of voters (adult population who are 18 or older) obtained from the electoral precinct-based sampling frame. Column 3 presents the difference between the population and the adult population in subsequent years. Although these two population 7
in returns to age|4.19|-24%|0.96|-6%|-0.54|4%|
|Change in returns to gender|-0.20|1%|-0.08|1%|-0.33|3%|
|**Returns to other characteristics**|||||||
|Urban|||||||
|Nonfarm|0.11|-1%|0.20|-1%|-0.40|3%|
|Farm|0.45|-3%|0.12|-1%|-0.09|1%|
|Regions|||||||
|Nonfarm|-0.87|5%|-4.79|30%|-0.27|2%|
|Farm|-1.82|10%|-0.18|1%|-1.62|13%|
|Land (Farm)|-7.25|42%|-3.18|20%|||
|<br>Irrigation (Farm)|0.30|-2%|-0.11|1%|||
|Other members (Farm)|1.48|-9%|1.07|-7%|||
|Constant|||||||
|Nonfarm|-14.87|86%|-3.38|21%|-2.71|21%|
|Farm|-7.74|45%|0.00|0%|-1.11|9%|

Source: Own estimates based on Peru's ENAHO 2004 -2010, Thailand's SES 2000 -2009, and Bangladesh's HIES 2000 -2010.

34
Policy Research Working Paper 10525

# **Abstract**

This paper analyzes extreme child poverty ($2.15/day poverty line) trends, as well as child poverty based on the higher international poverty lines of $3.65 and $6.85. The paper provides a trajectory of extreme child poverty (children living in extremely poor households) from 2013 to 2019 (based on the most recent surveys included in the Global Monitoring Database), complemented by nowcasting for 2020 to 2022. Children continue to be disproportionately affected by extreme poverty. Children who are younger than 18 years comprise more than 50 percent of those living in extreme poverty, although their share of the population is 31 percent. The paper estimates that in 2019, 15.8 percent of children in the world (319 million) younger than 18 years lived on less than $2.15 (2017 purchasing power parity) per day, as opposed to 6.6 percent of adults ages 18 and older. More recent “nowcasted” estimates suggest that at least 333 million children were expected to be living in extremely poor households in 2022, implying that 14 million more children were extremely poor in 2022 than in 2019. Following an increase in extreme child poverty at the height of the pandemic in 2020, nowcasted estimates show that the rate of extreme child poverty fell again in 2021 and 2022, but only at the slow rate of progress seen prior to the COVID-19 crisis. If the COVID-19 pandemic had not occurred, an estimated 79.7 million fewer children would have been living in extreme poverty between 2013 and 2022; however, the estimates suggest that the number of children living in extreme poverty decreased by 49.2 million, due to pandemic disruptions.

This paper is a product of the Poverty and Equity Global Practice; the Social Sustainability and Inclusion Global Practice; and the Development Data 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
depending on macroeconomic circumstances. In Turkey’s case, the financial scenario generator is constructed as a stand-alone module, so that it is possible to shift conveniently between different approaches without the need to adapt the remaining parts of TDSM.

Financial crises, regime changes and the significant market volatility that Turkey faced before 2002 create stationarity problems in retrospective time-series analysis. Since the country entered a new economic policy environment after the beginning of the millennium, it is difficult for any econometric model, based on long-term historical data, to reasonably describe future financial outcomes. In order to address this problem, the starting point of the historical financial data in input modeling was restricted to the beginning of 2002. Additionally, a forward-looking simulation tool that allows for user calibration was constructed. Another possible solution could be to adopt a deterministic approach and work with user-provided scenarios.

The variety of instruments in multiple currencies poses an additional challenge in scenario generation. The module is extended to cover yield curves not only for local currency bonds, but also for USD- and Euro-denominated international bond issuances.

The financial scenario generator makes use of time-series analysis and imposes random shocks to generate correlated paths for financial variables such as interest and exchange rates. In this module, the main objective is to forecast and simulate variables that directly affect Treasury debt dynamics and thus produce relevant scenario paths.

The variables that are forecasted and simulated in the Cost-at-Risk framework are as follows:

1. Term structure of interest rates

TRY yield curve USD yield curve Euro yield curve

# 2. Exchange rates

TRY/USD TRY/Euro 3. Inflation rate (consumer price index) 20
caveat to our survival results, however, is the reliance on time series variation, which leads to weak identification.

New research on the survival of formal firms in low-income countries has important implications for development strategies. In order for the private sector to act as an „engine of growth‟ and advance the development process, it is necessary for firms to survive and grow. It is also important for policy makers to understand and consider the impact of government policies on private sector survival and growth.

An important caveat is that our analysis covers all formally registered firms, but excludes firms that operate informally, even though the informal economy encompasses an estimated 72% of jobs in Sub-Saharan Africa (OECD, 2009).<sup>3</sup> Yet it remains important to understand job dynamics among formal sector firms, since the inability of economies to create legal jobs is one of the causes of the expansion of the informal economy. Data from the World Bank Enterprise Surveys of informal firms in three African countries suggests that most informal firms have less than 2 employees and unlikely to contribute to job creation. For instance, 46% of informal firms in Cote d‟Ivoire (as compared to 39% in Madagascar and 31% in Mauritius) start not because the owner sees a good business opportunity, but because the (largest) owner could not find alternative employment opportunities (“unwilling” entrepreneurs). Furthermore, 81% of firms in Cote d‟Ivoire (as compared to 72% in Madagascar and 49% in Mauritius) operate within household premises and few have paid non-family employees (World Bank, 2010b).<sup>4</sup> Furthermore, in general, labor productivity and TFP are higher on average in formal firms than in large informal firms, which in turn have higher productivity than small informal firms (Amin, 2009). The literature also highlights other potential advantages of formal sector participation for large firms, including police and judicial protection (and less vulnerability to corruption and the
Measuring True Sales and Underreporting with Matched Firm-Level Survey and Tax office Data calculated the percent of the variance in reported underreporting that can be explained by location and industry effects (‘Variance 1’ in Figure 1). If firms report the average amount of underreporting for firms in the same industry and/or locality, then a major part of the

**Figure 1: Underreporting by Firms around the World Measured by Direct Approach**

<!-- Start of picture text --> Mean Variance1 Variance2<br>0,80<br>Linear (Mean) Linear (Variance1) Linear (Variance2)<br>0,70<br>0,60<br>0,50<br>0,40<br>0,30<br>0,20<br>0,10<br>0,00<br>COUNTRY sorted by log GDP per capita (2005)<br>Ethiopia Malawi Mozambique Tajikistan Bangladesh Cambodia Laos Kyrgyz Rep. Kenya Uzbekistan Vietnam India Nigeria Moldova Nicaragua Mongolia Egypt Bolivia Paraguay Georgia Honduras Azerbaijan Armenia Korea Rep. Ukraine Guatemala Albania El Salvador Bosnia & Herzegovina Peru Macedonia Belarus Colombia Bulgaria Serbia & Montenegro Kazakhstan Romania Panama South Africa Uruguay Russia Argentina Turkey Latvia Lithuania Poland Mexico Croatia Estonia Hungary Slovakia Czech Rep. Portugal Slovenia Greece Spain Germany Ireland<br><!-- End of picture text --> observed variance in reported underreporting would be captured by location, industry and interaction effects. For the low income countries the percentage explained is 26.1% on average, for the lower and upper middle income countries this is 27.6% and 28.7% respectively, and for the high income and OECD countries they are 56.5% and 23% respectively. Hence, it appears that a major part of the variance in reported underreporting can be explained by differences in mean reporting of firms active in different industries 9
where TFPR is the measure of productivity described above and T<sup>UP</sup> is a measure of input tariffs at the sector level, which is described below. All our specifications include firm- and time-fixed effects, and sector time trends. We also control for output tariffs (T<sup>O</sup> ), share of exports at the firm level, and lagged FDI flows in the sector. The model is estimated as a standard linear fixed effects estimator, all independent variables are lagged on period, and standard errors are clustered at the sector level.

A second specification (equation 2) considers FDI in upstream sectors. We first control for FDI in upstream manufacturing sectors, , also described below, to account for other trade<sup>,MM</sup> liberalization reforms that could potentially confound the effects of input tariffs. Second, we include TTF<sup>U</sup> a measure of FDI in upstream service sectors, , to capture vertical linkages from the openness<sup>,SS</sup> of services to FDI. We are particularly interested in the effect of this variable as listed firms are more TTF<sup>U</sup> likely to outsource services. Indeed, typical services feasible for outsourcing, such as sales, distribution, and other administrative costs, represent a significant share of production costs (11 percent) for these publicly listed firms.

where weights, _w_ , are fixed over time and constructed from the 2007 GTAP input-output (IO) table as input shares, which give a sense of the importance of each upstream sector in terms of input and service costs. Hence, the expectation is that firms using certain inputs or services more intensively benefit from the integration of these sectors. Our measures of upstream tariffs and FDI are, therefore, a form of Bartik instrument (in a reduced form setting) where IO weights reflect the differential exposure of downstream sectors to common shocks in upstream sectors. In this
data and measurement requirements we spell out in this paper are fulfilled in some surveys, such as the use of panel data, and detailed measurements about various themes. Needless to say, _not_ all Bank surveys should pay heed to the specific themes and hypotheses this paper mentions; we recognize that no survey can address all the concerns raised here given time constraints, _ad hoc_ purposes of specific surveys, and survey costs. We also recognize that our coverage in this survey of surveys is not comprehensive. Ultimately surveyors must decide how they wish to address the question at hand, and design questions accordingly. However, if a firm survey aims 35