rafmacalaba/data-use-mentions-extended huggingface dataset:
suggests that more girls than boys are hidden as students in primary schools, as the official age group for the primary school students is 7-12. This is corroborated by the results of the field visits.

Many parents in the selected research districts also noted that they decided to send their daughters to _madrasah_ following completion of primary school, and indeed in recent years _madrasah_ have been opening more seats for girls. Again, this is reflected in the literature. Some studies have pointed to parents preferring to send their daughters to _madrasah_ instead of public schools, given a preference for religious education and the ability of Islamic schools to provide values-based moral education to teach girls to become good mothers (Jackson & Parker, 2008).

For higher education (ages 19-24), the GPI for SER (School Enrollment Rate) at the national level has increased dramatically in recent decades. While the GPI in 1997 was 0.72, by 2007 it increased to 0.95. The GPI figure was about 1 from 2012 to 2014, and then stabilized above 1 during the 2015-19 period. Currently, female participation in higher education remains higher than male participation by 1 percentage point (Figure 3).

**Figure 3. Tertiary enrollment rate of population aged 19-24 by sex and Gender Parity Index**

<!-- Start of picture text --> 30 1.20<br>25 1.00<br>20 0.80<br>15 0.60<br>10 0.40<br>5 0.20<br>0 0.00<br>Male Female GPI<br>GPI<br>Percent<br><!-- End of picture text --> Source: Susenas various years.

At the subnational level, the SER for ages 19-24 is largely influenced by the availability of universities in the respective areas. For example, compared with the districts of Bangkalan and Sampang, Banjar Baru and Banjarmasin have more universities, and thus the SER for ages
13 funding and the monitoring of school quality, persuade families to send their children to school; private investment/GDP from _World Development Indicators_ , testing whether elections are sufficient to persuade governments to encourage private investment, including governments incentives to treat private investors opportunistically; and measures of bureaucratic quality and corruption taken from Political Risk Services‟ _International Country Risk Guide_ , to test whether elections discourage governments from acting arbitrarily towards citizens.

The measure of competitive elections is based on two variables in the Database of Political Institutions (Beck, et al. 2001), the legislative and executive indices of electoral competitiveness. These two indices are highest (equal to seven) when elections have multiple competitors, none of whom receives more than 75 percent of the vote. When both indices are equal to seven, the competitive elections variable is equal to one. Otherwise, it is zero. The regressions are estimated with ordinary least squares; standard errors are robust and clustered.

The all-country results in Table 1 indicate countries with competitive elections exhibit gross secondary school enrollment approximately 3 percentage points higher than countries without them, though the effect is not significant. Primary school enrollment is significantly larger, 5.6 percentage points, in countries with competitive elections. In contrast, private investment is no different in countries with competitive elections. The association between elections and bureaucratic quality and corruption is statistically significant: they are approximately one-third of a standard deviation higher (better) in countries with competitive elections.
to build a better and compelling identification of the impacts of trade policy on the skill premium. Overall, we find that tariff liberalization has hurt all workers but has caused an increase in the skill premium. In particular, we find that the large tariff cuts of the 1970s —of nearly 70 percentage points— can explain up to one-fourth of the observed increase in wage inequality (which doubled from 1974 to 1982). Instead, the liberalization of the 1990s —of roughly 12 percentage points— can only explain 10 percent of the increase in the skill premium (of 42 percent). This indicates that tariff reforms have contributed to the observed increase in wage inequality but that there are other concurrent factors, particularly during the reforms of the 1990s, that played a significant role too.

# Data Appendix

In this Appendix we document the construction of the tariff measures used in the paper and we briefly describe the household surveys.

## Import Tariffs

Our goal was to construct a measure of ad-valorem import tariffs in place in Argentina in each year during the 1974—2001 period. The source for the raw data is the Guía Práctica del Exportador e Importador, a monthly publication that provides current tariffs at the most disaggregated level of the National Import Tariff Classification (NADI). Table A1 describes the tariff definition used for different periods. Notice that these different periods are characterized by different institutional arrangements (like the adoption of Mercosur in the 1990s) and different trade related policies. For each year, we use the tariffs in place in the middle of the year.

We needed to match the tariff information from the customs sources (the Guía Práctica) with the industry data from the household survey, which is based on the ISIC industry classification. From 1974 to 1991, the Argentine Statistical Institute used the ISIC Revision 2, and moved to the Revision 3 afterwards. Hence, we matched each category in the NADI with its closest equivalent in the ISIC classification used in different period. Since the 17
To offer a cross-country and cross-regional perspective, we compare the Egyptian labor market transition matrices with those of Mexico. Mexico provides an interesting comparator country given its similar level of GDP per capita in purchasing power parity, its large private informal sector, its young labor force, and low female labor force participation—all of which are common characteristics with Egypt’s labor market.

Our transition matrices showcase particular dominant absorbing labor market states among women and men. Specifically, we find a large degree of persistence in being out of labor force among women. On the other hand, we find very low female labor market engagement in the private sector, be it formal or informal, while we observe an important female presence in the public sector and in particular, a persistence in female public sector employment among the most educated. As for men, we find suggestive evidence of high informality—in particular among the less educated— and a lack of dynamism to transition to the private formal sector or the public formal sector. Such transitions are only likely to occur among the most educated men.

The stickiness of these dominant absorbing states is further confirmed when contrasting Egypt’s transition matrices with those of Mexico. Relying on data from Mexico’s labor force survey, Encuesta Nacional de Ocupación y Empleo, and simulating labor market transitions over a six-year window using Monte Carlo simulations of repeated discrete-time Markov chains, our results highlight very divergent levels of dynamism between the two labor markets. Mexico’s labor market shows a large degree of dynamism over the six-year window regardless of individual initial labor market states at baseline, while Egypt’s labor market shows great persistence in labor market states and far fewer transitions between the two rounds.

22

**Official Use**
skills can be difficult to measure through existing national and international assessments. However, there are new measurement efforts starting in several ECA countries<sup>25</sup> that aim at assessing both the demand and the supply dimension of cognitive and non-cognitive skills. Firm surveys in Macedonia and Ukraine have found that in addition to technical skills, the lack of non-cognitive generic skills appears especially binding. Also in OECD countries and some middle-income countries, non-cognitive skills are as important as cognitive and technical skills in firms’ hiring decisions (World Bank, forthcoming).

Responding to an energy price shock, therefore, will require a combination of strong basic education systems that produce workers with portable skills, innovation policies that allow for easy adaptability of knowledge, and an environment in which individuals with key non-cognitive abilities such as entrepreneurship are rewarded (World Bank, 2011c).

Another vital area for ensuring labor market adaptability in the face of an industrial transition is the society’s scope for innovation. This concept can take many forms: from the creation of knowledge to its commercialization and acquisition from abroad to its dissemination and use.<sup>26</sup> Nevertheless, countries with advanced public and private systems of research and development (R&D) are better prepared to innovate their way toward a new industrial order. A labor force with a high degree of cognitive and noncognitive skills, paired with national systems that enable and encourage innovation, will respond more favorably in adapting to an energy price shock. It is no surprise that many businesses are born and jobs are created in places where talented well-educated individuals are matched with enabling environments and government policies that are friendly to innovation. Figure 14 shows those scoring well in these

> 25 See World Bank, 2010 for details on skills measurement. 26

> See Rodriguez (2008) for a more detailed discussion on innovation.

27
2008 and 2016, we note a large reduction in the gender gap between female- and male-headed households. The unconditional gender gap drops to 18 percent in 2016, while the conditional gender gap drops to 21 percent: a 32 percent reduction over 8 years. Further, when we control for whether the household farms any export crops, the gender gap becomes statistically insignificant in 2016. Thus, farming of export crops remains a very strong determinant of agricultural productivity: in 2016 the gender gap within food crops is significant at 19 percent, while that within export crops is no longer significant. These gaps must be interpreted as lower bounds of agricultural productivity gender gaps: the 2008 data provides information on the plot manager’s gender as well on the plot owner’s gender, and we are able to show that estimated gender gaps are even larger when using either of these variables – rather than household headship – to differentiate between male and female productivity.<sup>1</sup> The Oaxaca-Blinder decomposition shows that in 2008, the single largest driver of female-headed households’ lower agricultural productivity was the low number of household male laborers, closely followed by their lower adoption of export crops, and then lower use of non-labor inputs (pesticides and chemical fertilizer). The low number of household male laborers disadvantaged poorer female-headed households most, while for chemical fertilizer it is the converse. The poorest female-headed households also faced the lowest returns on factors of production. Overall, gender gaps are observed all along the productivity distribution in 2008; apart from being slightly larger in the bottom half of the productivity redistribution, they are remarkably stable. A second key finding is that while the lower use of non-labor inputs matters more for export crop productivity (chemical fertilizer does not matter at all for food crops, while pesticide use matters less), the lower use of household male labor matters more for food crops. Moreover, while higher female labor use helped to close the gap in food crops between 2008 and 2016, it did not matter
includes a decline in supply and a decline in household demand for various basic goods. The simulated drop in household demand (except for food and hygiene goods), and public investment is 40 percent compared to the baseline scenario (pre-COVID scenario). The study shows the poverty rate rising to 19.2 percent from the current 15.2 percent, pulling an additional 475,000 individuals below the income poverty line. The economic recession, with a growth rate of -4.4 percent estimated for 2020, is estimated to elevate multidimensional poverty (estimated using a monetary approach) from 13.2 to 15.6 percent.

The UNICEF study showed that the two months of confinement decreed in Tunisia, during March and April 2020, led to a loss of 7% of household income. Poverty is estimated to have increased over the same period from 14% to 18.5%, or nearly half a million new poor. Poverty among children under the age of 18 years would increase from 19% to 25%, or nearly 900,000 poor children. Mitigation measures taken by the government were not found to have significantly countered the increase in poverty. Moreover, the same study shows that a universal allowance of 1 dinar per day (US$0.37) for each child not covered by social security would reduce child poverty by 5 percentage points.

<mark>Measuring real-time impacts of any crisis (financial, pandemics or economic slowdowns) on poverty and inequality is complicated in most countries because of delays in conducting detailed household surveys. As a result, a complementary tool for monitoring crisis impacts through administrative data (e.g., unemployment, school attendance, social benefit applications, etc.) is to assess the potential welfare impacts of crises through simulation tools that can then be used to simulate such impacts on poverty and inequality in short periods of time.</mark> In general, the literature showcases a wide variety of approaches used to quantify the distributional impacts of shocks.
at the National Oceanic and Atmospheric Administration’s (NOAA) National Geophysical Data Center (NGDC), available from 1992 to 2013. The DMSP satellites observe every location on the planet each night at the local time from 8:30 pm to 10 pm. In processing, some restrictions are imposed to remove observations affected by sunlight, glare, moonlight, aurora as well as cloud cover, and readings that likely reflect ephemeral events (such as fire) are set to zero, leaving mostly man-made lights from human settlements.<sup>20</sup> In each satellite-year data set, light intensity is recorded as a Digital Number (DN) ranging from 0 (no lights) to 63 (top-coded), for every 30 arc-second output pixels (about 0.86 square kilometers at

> https://www.scmp.com/comment/insight-opinion/article/2130132/now-china-must-come-clean-about-its-nationaleconomic-data.

> 20 See Elvidge et al. (2009), Chen and Nordhaus (2011) and Henderson, Storeygard, and Weil (2012) for details.

10
rigidity, as suggested by the small magnitude of the estimated coefficients. In Uruguay, white collar workers are more likely to be subject to DNWR, in line with most of the models discussed in this section. In contrast, occupational dummies in the case of Brazil all have very small effects.

There is a strong negative association between establishment size and the probability of being subject to either form of rigidity. In Uruguay, the smallest establishments present a probability of being subject to DRWR (DRWR) that is 6.7pp (11pp) larger than the reference category (establishments with more than a 1,000 employees). In Brazil, size effects in the case of DNWR are of fairly small magnitude. In contrast, the likelihood that a worker’s wage is subject to DRWR in the lowest size group is about 6.5pp lower than if that worker is employed in one of the largest firms in the sample. This evidence appears to be against shirking models of efficiency wages, but is in line with estimates of DRWR in Belgium reported in Du Caju et al. (2007) and with the greater flexibility in compensation policies of larger firms found in surveys of wage setters in European firms (Babecký et al., 2009).

As expected, the wage level in the base year, as measured by the three wage quartile dummies, has little impact on the probability of being subject to DNWR. Perhaps surprisingly, however, we also find very little impact of the wage quartile dummies in the case of DRWR in Brazil, where a concentration of rigidity across low-earning individuals might have been expected, given the importance of the minimum wage. The main rationale for this result lies in the fact that some of the covariates included in the regression, most notably age, education, and occupation dummies, are capturing the wage level of workers. An unconditional regression of the incidence of DRWR on the quartile dummies yields a highly significant and sizable negative association: workers whose wage level is in the fourth quartile have
Grootaert (1995) explains the decline in the West Forest as largely a result of the decline in farm income. His analysis shows that West Forest export crop farmers suffered a decline of about 34 percent in farm income, due in part to the decline in the cocoa and coffee sales between 1985-1988 (table 4). **6** The quantity of cocoa sold declined by 51 percent per farmer, while coffee sales declined by 65 percent. In contrast, farm income of East Forest export crop farmers declined only by 18 percent, compared to a decline in cocoa sales of 11 percent and coffee sales of 33 percent.<sup>7</sup> The relatively better

> 6 The farm income figures reported by Grootaert (1995) in his Table 9 are redeflated using the INS deflators and shown in Table 4.

7 Our calculations of cocoa and coffee sales show a smaller decline in cocoa and coffee than Grootaert's. We omitted several large outliers where we suspected that the unit had been incorrectly coded as tons instead of kilograms, and discarded several observations where the unit of sale was nonstandard. The means are weighted and based on reported sales of all cocoa-growing households in the region. Based on our calculations, the survey data show that sales of cocoa by the cocoa-growing population declined by 3 percent in the East Forest between 5
occurs. This finding can be interpreted as evidence that mission areas have better connections with the central government, giving them an advantage in the competition to attract development projects. Electoral turnover breaks these ties temporarily, whereas incumbent re-election seems to strengthen them. The conclusion is that political favoritism is likely to play a role, but the analysis is not able to rule out other mediating factors.

The correlations reported in this article generate a number of questions, leaving ample room for further research. The attempts to test for aid effectiveness were not able to establish conclusive evidence, and this issue deserves more attention. Moreover, although the analysis has provided evidence of a political aid cycle, it was only possible to make conjectures about why it exists. Also, how the aid cycle actually matters for long-term patterns of aid allocation is not clearly understood. Lastly, the list of potential mechanisms discussed in this article is not exhaustive, and there may well be other explanations that were not considered, and therefore not tested, in the present study.

# **References**

- Alesina, A., & Dollar, D. (2000). Who gives foreign aid to whom and why? _Journal of Economic Growth_ , _5_ (1), 0005074v1, 33–63. https://doi. org/10.1023/a:1009874203400

- Alpino, M., & Hammersmark, E. M. (2017). _Lighting the Path: The Influence of Historical Christian Missions on Modern-day Development Aid Allocation in Africa_ (Working paper No. 47). AidData.

- Altonji, J. G., Elder, T. E., & Taber, C. R. (2005). Selection on observed and unobserved variables: Assessing the effectiveness of Catholic schools. _Journal of Political Economy_ , _113_ (1), 151–184.

- Andersen, T. B., Bentzen, J., Dalgaard, C.-J., & Sharp, P. (2017)
These results demonstrate that subjective questions on welfare that rely on heterogeneous scales across households might be ill-suited to precisely track how consumption and employment are changing during and after a crisis. These results suggest that in the case where a reference scale is not explicitly provided to a household, the scale itself could change following a crisis making it difficult to even identify the sign of changes in actual consumption, employment, and access to basic services.

Furthermore, the results demonstrate that the responses are sensitive to the specific wording of the subjective welfare question, where similar but different wording of scales (“satisfied” versus “enough”) can result in different estimates of the impact of a crisis. And lastly, the results are also sensitive to who in the household responds to the subjective questions on welfare. Overall, the results demonstrate the importance of standardizing scales in questions across households, or to focus on questions where the scales are identical across households.

The results are particularly relevant for the current high-frequency surveys being used to track welfare through the COVID-19 pandemic (e.g., World Bank 2020), and will also likely become more relevant in other settings as the need for high-frequency monitoring in conflict and fragile environments continues to increase (e.g., World Bank 2017b). Subjective metrics are often easier to collect than entire consumption modules; and including subjective welfare metrics that are official indicators tracking progress in achieving the Sustainable Development Goals in the high-frequency surveys can allow a comparison to the last household survey and to international benchmarks. Because of these advantages, many of the high-frequency surveys currently being conducted to track the COVID-19 pandemic include questions in which the scale is not explicitly introduced, and it might be difficult to interpret these changes to these questions as the pandemic evolves.

However, there are a number of questions that this analysis is unable to address. First, these are all short-term effects of the capture. The survey ended three months after the capture of the city, and it is unclear how long the impacts
and in and out of the region early on. Border closures and restricted trade with China, in addition to precautionary containment measures that were put in place at the onset of the pandemic had a profound economic impact on many EAP countries in the early months of 2020, before many other countries had felt its toll. Furthermore, despite being geographically close, many developing EAP countries had differing experiences during the pandemic in terms of the severity of local COVID-19 transmission, mobility restrictions, and economic impacts. For example, countries like Vietnam and Lao PDR had few COVID-19 cases throughout 2020 and after a brief period of mobility restrictions and an economic contraction in the first half of 2020, their economies rebounded relatively quickly until experiencing an uptick in cases in mid2021. On the other hand, countries such as Indonesia and the Philippines experienced a pandemic-induced health crisis early on which led to economic contractions that crippled the economy for much of 2020. Thus, the prolonged and varied impact of the pandemic across middle-income countries in the EAP region provides a befitting context to explore the distributional impacts of the economic crisis that have unfolded from the pandemic across different economies.

Our main source of data is the High Frequency Phone Surveys (HFPS), collected by the World Bank from May 2020 in collaboration with National Statistics Offices of numerous developing countries.<sup>2</sup> While these surveys are not without their caveats due to constraints of the mobile phone survey methodology,<sup>3</sup> they provide timely and useful indicative evidence on how COVID-19 has affected households, in the absence of standard labor force and expenditure surveys. Between May 2020 and June 2021, 11 countries in the EAP region administered the HFPS, of which nine implemented at least two surveys. The HFPS data analyzed in this paper<sup>4</sup> comes from seven of these countries and spans one year of the pandemic, from May 2020 to May 2021. Countries included in the analysis are Indonesia, Lao PDR, Mongolia, Myanmar