rafmacalaba/data-use-mentions-extended huggingface dataset:
components. Rubin and Tygesen (1996), Beine and Hecq (1997) and Beine, Candelon and Hecq (2000) use a codependence framework. Filardo and Gordon (1994), Beine, Candelon and Sekkat (1999) and Krolzig (2001) use a Markov Switching VAR model. This empirical work demonstrates that it is important to distinguish between short and long-run effects. Bayoumi and Eichengreen (1993), Helg et al. (1995) and Rubin and Thygesen (1996) use differenced variables in the VAR representation. However, such a specification does not allow for long-run relationship between the variables. Beine and al. (2000) overcome this by investigating simultaneously common trends and common cycles, where evidence of a common European cycle is taken as evidence of perfect synchronization of shocks. Breitung and Candelon (2001) use a frequency domain common cycle test to analyze synchronization at different business cycle frequencies.

We use annual data from 1965 to 2002 for real GDP and trade figures, and monthly data on industrial production and economic activity from 1995 to 2002. GDP data is from IFS, data on industrial production are from Central Bank statistics. Trade data is from WITS and Direction of Trade Statistics.

The key variable in our study is the degree of business cycle synchronization between countries _i_ and _j_ . To measure this variable, we follow Frankel and Rose (1998) and compute the correlation between the cyclical component of the output in countries _i_ and _j_ , where a higher correlation implies a higher degree of business cycle synchronization. The cyclical component of output is obtained using different de-trending methods. Given the lack of consensus on the optimal procedure and the sensitivity of the cycle to the de-trending method, this approach should provide a robustness check of our results. For annual data we use first-differencing and bandpass filtering (Baxter and King, 1999). Spectral analysis is used to assess business cycle synchronization with monthly data.

# **2.1.1 Annual Data: 1965 –2002**

Band pass filtered data
,841|<br>124,009|
|... soybean (ha)|68,504|19,754|24,823|477|947|22,503|1,502|6,750|9,238|51,014|
|... rapeseed (ha)|41,068|11,366|5,977|11,295|983|11,447|70|1,354|4,677|34,967|
|<br>... other (ha)|28,331|7,870|11,042|3,501|1,046|4,872|549|3,310|7,207|17,265|
|No. of obs. (farms)|1,919|915|260|408|112|224|476|693|460|290|

_Source:_ Own computation from MAPF/WB/EU small producer survey _Note:_ Data are for the balanced panel of farms that report cultivating any crop in both the 2021 and 2022 agricultural seasons.

24
of publication bias in the literature that could overemphasize this positive impact of ICT on productivity. According to Polak (2014), the average estimated ICT elasticity is positive but very small, 0.3%. One possible explanation for this small impact could be the fact that ICT is embedded in many capital and production technologies, which makes it difficult to separate the sole impact of ICT related investments from the impact of production factors.

The objective of this paper is twofold. First, it aims to contribute to the existing empirical literature on the impact of ICT on productivity. Our approach focuses on one specific channel through which ICT can impact productivity: _ICT as an innovation enabler_ . Second, and more importantly, our objective is to shed light on the role of ICT in firm level productivity for a sample of African countries. The current low productivity levels in African firms suggest that the enhancing effect of ICT could be potentially large. However,

> 2 Solow (1987) has remarked that “computers were everywhere but in the productivity statistics”.

> 3 Other factors are: some of the benefits of ICT were not picked up in the productivity statistics; benefits of ICT use took a considerable time to emerge, as some other key technologies, such as electricity; small samples of firms drawn from private sources.

> 4 This has been pointed out by a recent article from The Economist (2014).

2
# **X. CONCLUSION**

This paper presents a skills index for developing countries in Asia as a first step towards developing a Global Skills Index. The Asian Skills Index presented here is roughly modeled on the European Skills Index but includes features specific to developing countries in Asia such as “vulnerable employment” and “unemployment among the highly educated”. In contrast to the ESI, we explicitly incorporate learning adjusted schooling as measured by LAYS.

Macroeconomic research on the relationship between human capital and economic growth has focused on several proxies for human capital over the years, including earnings, years of schooling, cognitive scores and most recently, LAYS. However, all these measures have some shortcomings. LAYS is based on internationally comparable tests, which primarily measure cognitive skills. Such skills are clearly valuable in developed countries. In developing countries, however, the requisite skills and economic requirements of labor markets are different. Metrics like LAYS are clearly valuable (as our own work here indicates) but LAYS by itself means little if schooling, and the skills it engenders, does not translate into productive employment. The Asian Skills Index developed here considers not just learning but also the process by which skills translate into economic output. By embedding traditional measures of education and schooling quality within a broader skills system, the paper provides crucial economic context to the human capital enhancement process.

There are several directions in which future research in this area can proceed. One of the most significant has to do with economic indicators that by their very nature can be intrinsically misleading. Nepal’s total unemployment rate, for instance, is lower than average. However, this masks the fact that many of the jobs are classified as “vulnerable employment”. Tajikistan, on 31
Figure 1. Survey-based measures of inflation expectations: Country evidence A. 1-year-ahead inflation expectations, selected advanced economies, average <!-- Start of picture text --> Percent Professional<br>5 Household<br>4<br>3<br>2<br>1<br>0<br>United States Japan New Zealand<br><!-- End of picture text --> C. Volatility of 1-year-ahead inflation expectations, selected advanced economies <!-- Start of picture text --> Professional<br>2<br>Household<br>1<br>0<br>United States Japan New Zealand<br><!-- End of picture text --> E. 5-year-ahead inflation expectations, selected advanced economies, average <!-- Start of picture text --> Percent<br>Professional<br>5 Household<br>4<br>3<br>2<br>1<br>0<br>United States Japan New Zealand<br><!-- End of picture text --> B. 1-year-ahead inflation expectations, selected EMDEs, average <!-- Start of picture text --> Percent Professional<br>12 Household<br>10<br>8<br>6<br>4<br>2<br>0<br>India Philippines South Africa<br><!-- End of picture text --> D. Volatility of 1-year-ahead inflation expectations, selected EMDEs <!-- Start of picture text --> Professional<br>3 Household<br>2<br>1<br>0<br>India Philippines South Africa<br><!-- End of picture text --> F. 5-year-ahead inflation expectations, South Africa <!-- Start of picture text --> Percent<br>7.0<br>Analysts Business Labor<br
<u>(www.worldpop.org, 2018), Global Rural-Urban Mapping Project, Version 1 (GRUMP) (CIESIN et al.</u> 2011), Gridded Population of the World Version 4 (GPWv4) (CIESIN, 2017), Gridded Population of the World, United Nation (GPW UNEP, 2006) and Global Human Settlement Population Grid (GHSPOP) (JRC and CIESIN 2015). However, none of these data sets on its own was sufficient for our purposes, as they were created without the use of the PESS 2014 data, or the final total population was not adjusted to match the PESS regional total. In addition, we had access to more recent data sets (highresolution DigitalGlobe population estimates), which we wanted to use to ensure that our EA delineation of Somalia is based on the most up to date population estimates. Therefore, we produced a novel 100m x 100m population density map to calibrate our EA delineation. We give below an only succinct overview of the method employed as it is not the object of the present paper, and it is not relevant to the description and results of our novel automated process for EA delineation, which can accept as input any gridded data set of sufficiently high resolution. Appendices 2 and 3 list the data sources that we used for urban and rural areas respectively, and the transformations we applied in order to obtain a 100m x 100m raster for each. Data sources include information on building density, household density and population density. We used the World Bank survey (UNFPA 2014) to estimate a median number of people per building and per household to approximate population density from data on building and household densities. In places lacking data but identified as settled, we modeled population density based on the distribution of population estimates in similar settlements. We then set population density to zero in locations known to be not settled, and to a low value in locations that could be settled but for which we have no data (around known settlements
.<sup>44</sup> Table 7 shows that the relative fertility decline in firstborn-girl families is driven by a shift from having three or more children toward having two or fewer children.

Note that our fertility and EFM results remain similar if we restrict the NFHS sample to rural households from the states included in the REDS sample. Moreover, our results in Table 6 are robust to restricting the sample to women that have likely completed childbearing (i.e., women of age _≥_ 33 or _≥_ 37 at the time of survey), easing concerns about censoring, which may be more severe among firstborn-girl relative to firstborn-boy households in the post-period, driving our results. As a robustness check, we also estimate the effect on the hazard of birth using a Cox proportional hazard model. Table A.13 reports the hazard ratios from this exercise; our results remain the same.

# **6.3 Actual vs Desired Fertility**

Since sex-selective abortion allows parents to avoid unwanted children, we also test whether availability of ultrasound drove actual fertility closer to desired fertility. Our estimates in Table 6 imply large reductions in undesired fertility, which the first row shows was 0.115 births in firstborn-girl relative to firstborn-boy families. The coefficients of interest (those on the triple-interaction terms) are similar for actual fertility and for excess fertility (actual minus desired fertility). This again

> 43These results are available upon request. We find no significant change in boy survival alongside increases in girl survival in firstborn-girl families, however.

> 44Using a logit specification in Table 5 yields very similar results.

20
per capita income is at the extreme poverty line (161,099 COP), women have a probability of participating in the labor market of 75% in 2019, compared to 95% for men, resulting in a 22percentage point difference. At the moderate poverty line (350,000 COP), the gap narrows to 17 percentage points, and in households with a per capita income of 1 million COP, the gap is reduced to 13 percentage points. Beyond that, the gap stabilizes at 10 percentage points. This finding is particularly significant as it highlights that in situations of poverty or vulnerability, men tend to assume income-generating roles while women's responsibilities revolve around caring for the home and family. Consequently, women acquire fewer or no skills and experience in productive sectors compared to men, leading to greater economic dependence for partnered women. Again, the pandemic exacerbated this gap, but only in lower-income households.

Regarding schooling, the results at the mean in 2019 indicate a probability of approximately 70% for women with no education, implying a gender gap of around 30 percentage points compared to men in the same group (Figures 27-28). As years of education increase, the probability rises and the gap shrinks to around 30 percentage points. However, men consistently have higher participation rates at any level of education. During the pandemic, women with less than 15 years of education were less likely to participate, with activity levels decreasing as the level of education approaches zero.

When examining participation gaps by age groups, both the CMEs (Figure 29) and AMEs (Figure 30) reveal higher gaps in labor market participation for older age groups (+20 percentage points), while for individuals in their 20s, the gap is around 10%. Although it is positive that the intergenerational gap has reduced, it is concerning that it still persists. Older women were particularly impacted during the pandemic, likely due to their predominant roles in caregiving activities and household chores.

For women residing in rural areas (Figure
surveys of companies included in a recently completed preCOVID World Bank Enterprise Surveys (WBES) to measure the impact of the COVID-19 pandemic on the private sector. We analyze only the loss of a “permanent” job, because a firm’s workforce level is the only jobrelated outcome that is effectively captured in the CFUWBES, and a job loss represents a more costly outcome for workers than other effects, such as reduced hours, wages, or temporary furloughs. In addition to firms’ permanent workforce adjustments, the CFUWBES questionnaire includes variables capturing the operations of the business, sales, liquidity and insolvency, firms’ technological adaptations, their expectations and uncertainty about the future, and public support received during the crisis. The WBES sampling approach is from the population of all registered establishments with five or more employees in manufacturing, retail, and other services sectors, designed to represent core productive sectors typically owned and operated privately. It does not include financial services, real estate and rental activities, or public service providers. Therefore, while it represents a major portion of the formal private sector, it does not capture all formal private sector employers. For Jordan, the baseline ES contains a total of 601 firms conducted from December 2018 to November 2019. The CFUWBES wave 1 (henceforth CFUWBES1) was conducted from July to August 2020, and the CFUWBES wave 2 (CFUWBES2) data was collected from November 2020 to January 2021. Both rounds of CFUWBES use the same sample as the most recent WBES to construct a panel. For Georgia, the baseline WBES contains a total of 701 firms interviewed between March 2019 and January 2020. The same firms were re-contacted in June 2020 for Wave 1 and in October/November 2020 for Wave 2.

In addition, we use recent labor force surveys to link firm data and predict potential effects on workers. For Jordan, we use the Jordan Labor Market Panel Survey (LMPS) 2016, which collects labor market information 7
# **_2.2 Descriptive Statistics on Student Achievement_**

The mean reading performance of the eight considered countries in PIRLS, reported in Table 1, clearly reveals the division into the two groups. The group of relatively “low performing countries” (LPC), Argentina, Colombia, Turkey and Macedonia, shows a mean performance between 418 and 449 achievement points (AP), while the group of relatively “high performing countries” (HPC), Germany, Greece, Italy and England, performs between 525 and 553 AP.<sup>7</sup> As the lowest performer among the selected countries, Argentina scores 4 AP lower than Colombia, 24 AP lower than Macedonia, 31 AP lower than Turkey and 122 AP lower than the HPC group. The size of these performance gaps can be illustrated by the unconditional difference of 42 AP between third graders and fourth graders in Sweden, which was the only country in PIRLS that tested third graders in addition to fourth graders. This difference can be interpreted as the additional value-added of one school year in Sweden, since both grades are tested in representative samples. Thus, the Argentine and Colombian performance is the equivalent of one-half to two-thirds of a (Swedish) school year behind the Macedonian and Turkish performance, and roughly three years behind the HPC group.

The mean performance hides substantial differences in the performance distribution between countries, though. The performance variation is much larger in Argentina than in all the countries of the HPC group. This is independent of the measure of performance variation, which include the standard deviation in national test scores (either absolute or relative to the national mean) and the performance difference between percentiles such as the 90<sup>th</sup> and 10<sup>th</sup> or the 75<sup>th</sup> and 25<sup>th</sup> . Only Macedonia shows a spread in performance that is similarly large as in Argentina, while depending on the measure
the combined effect of the collapse in domestic production, sanctions, devaluation and increasing reliance on imports of basic commodities.

_Figure 9: GDP deflator and CPI growth index (2003=100)_ <!-- Start of picture text --> 3300<br>3000<br>2700<br>2400<br>2100<br>CPI GDP Deflator<br>1800<br>1500<br>1200<br>900<br>600<br>300<br>0<br><!-- End of picture text --> _Source:_ WB staff calculations based on WDI and CBS _Based on these considerations, the most appropriate approach to project welfare growth would be to use per capita GDP in local currency units deflated using the CPI._<sup>_26_</sup>

# 4.1.2 Population estimates

Data challenges also affect population estimates, which are needed to convert macro aggregates in per capita terms.

The last population Census in Syria was conducted in 2004. Since then, population estimates for Syria have been revised multiple times. Pre-conflict population estimates and projections to account for the tremendous impact of conflict which claimed the lives of at least 350,209 individuals<sup>27</sup> and forced millions of Syrians to leave the country.<sup>28</sup> As shown in Figure 10, sizeable differences exist between different revisions of Population Prospects data post conflict. In particular, the latest available estimates from the 2022 Prospects revision – which have been recently adopted in WDI – place the Syrian population for 2022 at about 22 million, in line with direct population counts from HNAP and with the 2015 Prospects revision, but as much as 15 percent higher compared to the 2019 Prospects revision. Moreover, while population estimates for most recent years are relatively similar between the 2015 and 2022 population prospects revisions, major differences emerge for the years between 2009 and 2015.

> 26 See Section 4.2 and Section
# Figure 17. MNA: Policies to raise potential growth

<!-- Start of picture text --> A. Potential growth and contributions<br>Percent<br>6 Baseline Demographic trends Other factors<br>4<br>2<br>0<br>2011-21 Factor 2022-30 2011-21 Factor 2022-30<br>EMDEs MNA<br><!-- End of picture text --> C. Climate change scenarios

# B. Reform scenarios

<!-- Start of picture text --> Percent Social benefit reform<br>6 Labor market reforms<br>Education and health improvements<br>5 Investment surge<br>Baseline<br>4<br>3<br>2<br>2011-21 2022-30 Reform 2011-21 2022-30 Reform<br>impact impact<br>EMDE MNA<br><!-- End of picture text -->

- D. Female labor force participation scenarios

<!-- Start of picture text --> Percent Investment boost (climate change) Percent<br>6 Disasters (climate change) 4 Baseline Female LFPR improvement<br>Baseline<br>5<br>3<br>4<br>2<br>3<br>2 1<br>2011-21 2022-30 Reform 2011-21 2022-30 Reform<br>impact impact<br>0<br>EMDE MNA 2011-21 2022-30 Reform<br>E. Share of oil revenue in oil exporters F. Climate risk<br>Percent of total revenue Occurrences per year<br>90 25 GCC<br>85 20 Other oil exporters<br>Oil importers<br>80 15<br>75 10<br>70 5<br>65<br>0<br>60 2006 2007 2008 2009 2010 2011 2012 2013 2014 2015 2016 2017 2018 2019 2020 1960s 1970s 1980s 1990s 2000s 2010s 2020-21<br><!-- End of picture text --> Sources: EM-DAT database;
.011)|(0.010)|
|<br>Crawling*current account|||||<br>-0.004|<br>-0.003|
|<br>deficit*2009 dummy|||||(0.009)|(0.008)|
|<br>constant||0.516**||0.553***||<br>0.354**|
|||(0.154)||(0.154)||(0.169)|
|**Pseudo R-Squared/R-**|0.089|0.111|0.083|0.105|0.101|0.127|
|**Squared**|||||||

***,**, * Significant at 1,5, and 10 percent level.  T-statistics in parentheses. Source: Authors‘ calculations based upon data from the World Bank Enterprise Survey,  BEEPS data and from other sources Note:   Since dependent variables are dummy variables, probit regressions are used.  Performance variables used in analysis in Table 3 are omitted because they were not available for all years. Additional controls used in Table 4 for firm characteristics, country characteristics, firm performance, and year dummies are also included in these regressions, but are not presented in the table to conserve space.  All regressions

47
# **Table A7. Average Annual Wages of SCP Graduates, by Country**

|**Average wage**|**Brazil**|**Colombia**|**Dominican**<br>**Republic**|**Ecuador**|**Peru**|
|---|---|---|---|---|---|
||**(1)**|**(2)**|**(3)**|**(4)**|**(5)**|
|In dollars (USD PPP 2019)|$10,730|$10,313|$11,275|$11,910|$7,481|
|In terms of the minimum<br>wage|2.2|1.3|1.3|1.4|1.3|

_Source_ : Own calculations using WBSCPS and administrative data.

_Note:_ This table reports the average annual wage per country, expressed in purchasing power parity (PPP) dollars or as a multiple of the country’s annual minimum wage. Average wage in terms of the minimum wage (MW) is calculated using each country’s MW in 2019 (Brazil, Ecuador, and Peru) and 2020 (Colombia and Dominican Republic). Average annual wage in terms of minimum wages = average annual wage / annual MW, where annual MW = monthly MW * 12. Percentiles 1 and 99 are excluded from these calculations. Statistics are weighted by WBSCPS sampling weights. Only São Paulo and Ceará are included for Brazil, and licensed programs for Peru.

71
The Structure of Social Disparities in Education: Gender and Wealth<sup>1</sup>

# I) Introduction

Universal primary education was enshrined as a human right in the United Nation's Universal Declaration of Human Rights in 1948. Forty years later the goal was still not in sight and a call on donors and governments to reaffirm their commitment to universal primary enrollment was part of the World Declaration on Education for All issued in Jomtien, Thailand in 1990. The year 2000 was set as the target for achieving this goal. It is now 1999 and we are still not near to achieving universal primary education - and as pointed out dramatically in a recent report by Oxfam International (1999) we do not appear to be closing in on it.

This paper uses a collection of internationally comparable household datasets to investigate the correlates of educational enrollment and attainment gaps within countries. The data from the Demographic and Health Surveys (DHS) for 57 surveys in 41 countries are used to carry out country specific analyses, which are comparable across countries. Specifically, the effects of gender, household wealth, the education of adult household members, and the presence of schools in the community on the educational outcomes of children are assessed in each country and compared across countries.

Using household based surveys allows the analysis to go beyond comparing country aggregates which are reported in several large "international databases" (e.g. UNESCO data or derivatives thereof such as Barro and Lee, 1993; Nehru, Swanson and Dubey, 1993; Dubey and King, 1994; Ahuja and Filmer, 1996). The DHS have a drawback in that they lack data on household consumption expenditures, the usual variable used to rank households by their socio-

> ' This paper has benefited greatly from comments from Jere Behrman, Jeff Hammer, Elizabeth King, Julian Lampietti, Andrew Mason, Lant Pritchett, Martin Ravallion, Jee-Peng Tan and participants at a workshop on Gender and Development in June 1999. Errors are of course my own. Please see http