rafmacalaba/data-use-mentions-extended huggingface dataset:
comes with its limitations, notably: less representative sample at the individual level<sup>4</sup> and lack of pre-pandemic baseline data. This does not allow a direct comparison to employment trajectory before the pandemic.

This paper instead relies on a nationally representative labor force survey that extends multiple years prior to the pandemic, which allows us to establish counterfactual employment outcome trends and to improve the precision of our COVID-19 impact estimates. To the best of our knowledge, this paper is the first to provide quantitative assessments on the causal impact of the pandemic on female and male employment in Indonesia.

The paper is structured as follows. Following Section 1 (this introduction), Section 2 presents the contextual background of the COVID-19 spread in Indonesia. Section 3 then presents a summary of existing studies on the COVID-19 pandemic’s gendered impact on employment. Section 4 discusses the data used in this study. Section 5 outlines the empirical strategy that this paper uses in measuring the pandemic’s effect on female and male employment in Indonesia. Section 6 presents the research findings, and Section 7 concludes.

# **2. COVID-19 Pandemic in Indonesia**

The COVID-19 outbreak hit Indonesia two months after it was first detected in late December 2019 in Wuhan, China. On March 2, 2020, President Joko Widodo officially announced the country’s first two COVID cases detected in Jakarta, and the country subsequently announced its first COVID death on March 11. By mid-March, the outbreak had spread to other provinces outside Jakarta, with authorities confirming multiple COVID cases in West Java, Special Region of Yogyakarta, Bali, West Kalimantan, and North Sulawesi. By the end of August 2020, Indonesia

> 4 Brubaker, Kilic, and Wollburg (2021) propose a recalibration mechanism to reweight observations in HFPS. While the reweighted estimates get closer to the true population averages, they still fail to overcome selection biases.

6
. _p_ -values reported in braces are computed using randomization inference (randomization- _t_ ). _p_ -values reported in parentheses are adjusted for testing each null hypothesis (null impact of API Original, API Plus, and the comparison) for the two different families of outcomes (survey-based and administrative data) through the stepwise procedure described in Romano and Wolf (2005a,b, 2016). All _p_ -values account for clustering at the school level.

While the difference with respect to the _Original_ modality is not statistically significant, the larger effect of the _Plus_ modality is consistent with qualitative evidence documenting that mentors with enhanced training shared more effective strategies to best deal with children’s emotions during the bimonthly peer-to-peer sessions (see Appendix A.3). The effect size of the _Plus_ modality on the GLS-weighted index of achievement is very large, 0.37 standard deviations—precisely estimated, and statistically different at the 95 percent level from the effect of the _Original_ modality.<sup>9</sup> The last two columns in Table 2 report the estimated effects on the average transition rate to secondary school. Less than two-thirds of the sixth graders in the control group enroll in seventh grade, while the corresponding national average is 95 percent. The _Plus_ modality

> 9In Table B-7 we report the results by sub-domains of the reading scores (panel A), math scores (panel B). While the estimates are erratic and not statistically significant for the _Original_ modality, the _Plus_ modality is shown to increase students’ proficiency in reading across various domains (familiar-word reading, reading comprehension, and dictation). There are no improvements in sound-related questions (initial sound and initial name), which is probably due to the fact that children whose first mother tongue is an indigenous language might struggle to capture Spanish alphabet pronunciation. For math scores, the _Plus_ modality seems particularly effective on numbers’ identification and discrimination as well as additions. There are no improvements in more involved tasks such as problem solving and shape recognition
consideration of the treatment of low incomes in household surveys. As discussed in Section 3, by virtue of being based on z/yi, which makes them distribution sensitive, our measures also are sensitive to mismeasurement of very low or, at the extreme, zero incomes. In this paper, we bottom-coded income at $0.50 per day, at which we arrived by applying the consumption floor methodology outlined in Ravallion (2016) to estimate minimum consumption levels. Further work is needed on different approaches to address the issue of mismeasurement of income (or consumption) at the bottom of the distribution.

******************************

**_Contributors_** : _Sterck had the original idea for the welfare index, which aggregates individual contributions to the index in the form of_ z/yi _. Decerf, Kraay, Özler, and Sterck drafted Sections 1 and 2, which were revised by all authors. Jolliffe, Kraay, Lakner, and Yonzan drafted Sections 3 and 4, which were revised by all authors. All authors interpreted the data, contributed to writing the paper, and approved the final version._

35
POLICY **RESEARCH WORKING PAPER** 1758

# **Summary findings**

Evaluating the restructuring of large enterprises in transition economies is difficult because it is only one of many economic changes. Such evaluation is nevertheless essential for designing reform policies.

Djankov and Pohl examine 21 case studies of Slovak firms based on detailed financial information for 199196, and interviews with top management. Much of their sample was firms initially classified as "nonviable lossmakers." They found that the majority of large Slovak firms successfully restructured without the help of foreign investors or government restructuring programs.

Privatization to insiders, through managementemployee buyouts, did not hamper restructuring because the new owners (old managers) invested heavily in new technology, laid off a substantial part of the workforce, sought foreign partnerships, and were prepared to sell controlling stakes to outsiders in return for fresh financial resources.

The evidence also suggests that mass privatization did not result in weak corporate governance because it was followed by a rapid consolidation of ownership.

Their findings support the view that the main objective of privatization programs should be the speedy transformation of ownership, not the selection of perfect owners.

Slovakia was an interesting choice for case-study analysis because much of the heavy industry and arms industry of former Czechoslovakia was located in Slovakia, so it inherited a relatively unattractive industrial structure. Slovakia also implemented two very different privatization programs, one of mass privatization and one of leveraged management buyouts or direct sales to (domestic) outside investors.

- This paper a product of the Finance and Private Sector Development Division, Europe and Central Asia Technical - Department is part of a larger effort in the department to study the determinants of enterprise restructuring in transition economies. Copies of the paper are available free from the World Bank, 1818 H Street NW, Washington, DC 20433. Please contact Faten Hatab, room H8-087, telephone _202-473 -5 83 5,_ fax 202-477-8 772, Internet address fhatab@worldbank.org. April 1997. (28 pages)

_The Policy Research Working Paper Series disseminates the findings
7</sup> We use a two-stage clustered design wherein PSUs or clusters are selected within each domain and then a sample of 10 households is selected within each cluster. Our decision to select 10 households within each cluster is consistent with the design of other large-scale household surveys conducted in developing countries. Under simple random sampling, the minimum sample size required for each state is as follows: where _w_ ̄ _s_ and _σs_ represent average household income per capita and the standard deviation of per capita income for state _s_ , respectively.

The minimum sample size under simple random sampling must be adjusted to account for the clustered design. The design effect due to clustering is accounted for by estimating the intracluster correlation of per capita income within each state. The correlation estimates can then be used to adjust the above sample sizes for the clustered design as follows:

> 5Income is defined as money received from work performed during the course of the reference period by individuals aged 12 or older.

> 6This is done to ensure some missing values are present in the data used but not as many as in the original data.

> 7The desired precision of 7.5 percent is somewhat arbitrary, but corresponds to precision targets in similar surveys and yields samples of reasonable sizes.

5
2018 (age, gender, whether the student has children of her own, family of origin’s socioeconomic score, and mother’s education level.) Note that the subset of students to which we were given access may not be a representative sample of all students who took the test. Further, the scores of the _Ser Bachiller_ test were not shared with us.

**_Labor Market Outcomes._** Their source are individual-level records from the Ecuadorian Social Security Institute ( _Instituto Ecuatoriano de Seguridad Social,_ IESS) and the Ecuadorian Internal Revenue Service Unit ( _Servicio de Rentas Internas,_ SRI), which together yield the universe of individuals formally employed or self-employed, with monthly records of individual employment status and earnings between January 2018 and December 2020. We use these to construct labor market outcomes for the 12-month periods before and after graduation.

The final dataset for Ecuador is a sample of 2019 SCP graduates. It includes individual characteristics (gender, age, whether the student has children), socioeconomic background (mother’s education level and socioeconomic index), pre-graduation labor market outcomes, and post-graduation formal employment status and wages. We merge the individual-level dataset with information at the program and HEI levels from the WBSCPS and administrative sources. The resulting dataset includes 1,239 individuals and 92 programs (relative to the 245 programs with effective surveys in our sample).<sup>18</sup> It is worth emphasizing that our individual-level datasets for Brazil and Ecuador are different in that, for Brazil, we have information on all students who entered our survey programs in 2014

> 18 Some programs do not match to individual-level data for reasons similar to those in Brazil. In addition, some programs match but only have one student. Table A9 presents descriptive statistics for the subsample of the 92 surveyed programs that match to individual-level data and shows t-tests for mean differences between these programs and the 153 unmatched ones with 2+ students. Some differences are statistically significant. Matched programs are more likely to
the same sector, d) banks’ ratings in different regions, and e) banks’ ratings for a particular instrument, etc. Thus it would be much easier to monitor and supervise.

For countries that wish to implement the CRB approach but be fully Basel II compliant according to the letter and not just the spirit of the proposals, then one possibility is to adopt the Standardized Approach for capital and use the CRB approach to define provisions. Under recent changes to the Basel II proposals, if the CRB approach is calibrated to give provisioning levels equal to the full “value at risk” minus the Standardized Approach’s capital requirement, then a country would have a fully Basel II compliant and rational system where banks’ reserving policies were roughly equal to the value at risk<sup>51</sup> . Such an approach would build very naturally on the types of public databases many countries have built up to monitor and enforce provisioning rules. This standard might be labeled Basel II-CRB.

There are then a group of developing countries that will indeed wish to implement the IRB approach - at least for a small group of larger and more sophisticated banks. These countries will of course need to dedicate substantial resources to consider carefully the IRB proposals, possibly recalibrate them for the task at hand, and monitor and supervise them effectively. There will also be a substantial cost for banks in these countries in terms of regulatory costs for the data and systems required. Only countries that score well in terms of BCP compliance will be in a position to fully reap the rewards of these more advanced approaches.

One criticism of Basel II has been that there are so many alternatives that the essence of a standard has been lost. It is hoped by defining the islands of a) Basel I, b) Basel II-a, c) Basel II-b, d) Basel II-CRN, and e) Basel II-IRB that countries may navigate more effectively through the sea of standards.

Finally, given the globalization of banking, Basel II will also
the incidence of complaints should be positively affected by intensity of exposure to each harmful pollutant. If only visible pollutants are significant, then complaints provide an incomplete damage index. Secondly, higher-income areas should have more complaints per capita because willingness-to-pay for environmental improvement increases with income. If the opposite finding holds, then complaints are a biased index of potential benefits from pollution abatement. Third, education should have no independent effect once controls are introduced for local income and pollution exposure. A positive effect would imply significant problems of information or, perhaps, 'intimidation' in poorlyeducated populations. In either case, complaints would provide a biased guide to agency resource allocation.

# 4. **Data Sources and Estimating Equation**

# **4.1 Data Sources**

For the empirical analysis in this paper, we have constructed a province-level panel database from official yearbooks available in China: **The China Environment Yearbook** (1987-1993) and **China Statistical Yearbook** (1987-1993). Specific sources of data are reported in Table 4.

# **4.2 Estimating Equation**

With composite parameters (X), (3.5) yields an estimating equation for the incidence of complaints. Since our provincial data on complaints are not divided into airand water-related categories, we introduce both air and water emissions densities: 9
# **1. Introduction**

The rise in unemployment during an economic crisis poses a significant concern to policy makers. Moreover, layoffs could slow down the economic recovery, since re‐hiring and training workers may be costly for firms. This is particularly true for workers who have acquired job‐specific skills. For these workers, it may be beneficial for firms to not let them go in the first place. However, as firms face lower demand for their products, they may not have the financial means to keep paying these workers, particularly in the presence of credit constraints, which are often exacerbated during a crisis. There could thus be a case for government intervention through wage subsidies, which could limit layoffs and speed up economic recovery by eliminating search and training costs. In fact, 22 countries around the world used some form of wage subsidy program to promote employment retention during the recent economic crisis.<sup>1</sup> Much of the existing literature on wage subsidies focuses on subsidies for disadvantaged workers or for the unemployed (Kaldor 1936, Layard and Nickell 1980, and Phelps 1994), providing mixed evidence as to whether subsidies can improve the employment prospects of such workers (see Katz 1998 and Betcherman et al. 2004 for summaries of the literature). To my knowledge, there is, however, no empirical evidence on whether wage subsidies during an economic crisis can reduce layoffs and whether they promote economic recovery. This paper starts to fill this gap by examining a wage subsidy program that the Mexican government implemented in response to the recent economic crisis. Under this program, manufacturing firms in certain industries were eligible for receiving wage subsidies if they decided to keep their workers instead of letting them go during the period of January 2009 to August 2009.

I use monthly administrative data on employment at the industry level from the Mexican Social Security Institute (IMSS), covering nine years before and four years after program implementation, along with propensity score matching to construct groups of eligible and ineligible
Basu, K. and López-Calva, L.F., 2002. Functionings and capabilities. Colegio de México.

Bebbington, A., 1999. Capitals and capabilities: a framework for analyzing peasant viability, rural livelihoods and poverty. _World Development_ , 27(12), pp.2021-2044.

Becerril, J. and Abdulai, A., 2010. The impact of improved maize varieties on poverty in Mexico: a propensity score-matching approach. _World Development_ , 38(7), pp.1024-1035.

Biggeri, M. and Ferrannini, A., 2014. Opportunity gap analysis: Procedures and methods for applying the capability approach in development initiatives _. Journal of Human Development and Capabilities_ , 15(1), pp.60-78.

Brody, C.M., De Hoop, T., Vojtkova, M., Warnock, R., Dunbar, M., Murthy, P. and Dworkin, S., 2015. Economic self-help group programs for improving women’s empowerment: A systematic review. _Journal of Development Effectiveness_ , 9:1, 15-40 Coast, J., Flynn, T.N., Natarajan, L., Sproston, K., Lewis, J., Louviere, J.J. and Peters, T.J., 2008. Valuing the ICECAP capability index for older people. _Social Science & Medicine_ , 67(5), pp.874-882.

Cox-Edwards, A. and Rodríguez-Oreggia, E., 2009. Remittances and labor force participation in Mexico: an analysis using propensity score matching. _World Development_ , 37(5), pp.1004-1014.

25
21

# **8. Limitations**

The initial pilot product is developed only for the paddy crop. Given the microclimatic conditions in Sri Lanka, it ideally needs to be used over small geographic areas. Additionally, a single product is applicable for a single crop in a season, for a given district – necessitating the development of multiple products as needed.

An issue faced in general for index insurance and particularly for a hybrid product, is the high dependence on several sources of data where limitations in data infrastructure hinder robust product development. For an index based on rainfall, reliable historical rainfall data and a continuous supply of contemporary data are needed to measure product performance. Apart from rainfall, the hybrid product will need tank level data, which, for example in Sri Lanka are usually from several sources, given that the tanks could be managed either by regional level bodies or national level ones.

The hybrid index product addresses negative basis risk associated with irrigated farmlands only to an extent. It is no doubt an improvement on the rainfall index. However, there still remains a residual negative basis risk for irrigated farmlands due to factors like their distance from the tanks – some may be very far away, some very close, and this affects their relative benefits from the tanks. Due to the lack of granular data on individual farm distances from tanks, along with other variables, the impact of such risk cannot be incorporated. Ongoing pilot efforts by IFC in digitization of data, may provide an opportunity to cluster farms in different groups, i.e. by specific irrigation types, their distances from tanks, etc. Once such information is available, the product can be modified to capture these additional parameters, which can further contribute to reducing negative basis risk.

Another limitation of the model itself is that storage capacity can be affected by factors such as a buildup of sediment, tampering with the irrigation infrastructure by external parties, etc. These operational limitations exist, but they can be mitigated to some extent by the structuring
average<sup>_† ∗_</sup>|
|Scaled deviation in GDD|The z-score for GDD|
|Maximum temperature|The average maximum daily temperature|

_Note_ : The table presents definitions for included weather variables and transformations from weather sources defined in Table 1.<sup>_†_</sup> Growing season determined for each country following FAO crop calendar (see Table A2).<sup>_∗_</sup> For variables when “long run” is referenced, long run is defined as the entire length of the weather dataset. While each weather source has a different start date, to ensure blinding all datasets were shortened to 1983, which is the latest start date of the data sources.

46
where, Y is the net turnover in manufacturing industry i for time period t measured at the 2-digit VSIC level (there are total 22 3-digit VSIC manufacturing industries); P is industry-wise pollution intensity and μ is industry fixed effects. In addition to 22 industry level effects, we also used fixed effects for dirty and clean industries, using the Mani and Wheeler (1998) classification. Labor productivity (L) is the average net turnover per employee. Capital productivity (K) is calculated by dividing the net turnover by the total stock of fixed capital. The coefficient of interest is β 3<sup>which captures the</sup> increase in production of dirty industries relative to clean industries during the 2000-2002 period. If domestic production does shows an increase in the dirty industries relative to cleaner industries, we would find β 3<sup>> 0. The results for equation (2) are presented</sup> in Table 2.

Second, we measure whether exports have increased in the dirty industries relative to clean industries between pre-2000 and post-2000 years (in the year 2000, USBTA came into effect). Based on Grossman and Krueger (1993), we estimate exports from Vietnam as a function of labor intensity (L), capital intensity (K), and pollution intensity (P). Similar to the specification of hypothesis 1, we use 2-digit VSIC level data for manufacturing industries to compare pre-2000 (1997-1999) with those immediately following trade liberalization (2001-2002). The regression model is: where, X is the export from industry i as a fraction of net turnover for time period t measured at the 2-digit VSIC level (there are total 22 3-digit VSIC manufacturing industries); T is the liberalization dummy that takes the value 1 for post-2000 years and 0 14