rafmacalaba/data-use-mentions-extended huggingface dataset:
informal, temporary/irregular, or both. Almost 30 percent of jobs were for the government or public enterprises, and 16.4 percent were in self-employment or family workers (on farm or off).

In Georgia, in contrast, 35 percent of jobs in 2019 were in self-employment and 22.7 percent in unpaid family work. Wage jobs for the public sector and for the private sector both comprised lower shares of jobs (17.2 and 24.6 percent, respectively) than in Jordan. Wage work for the private sector in both countries was _Table 3-2: Employment Structure, Jordan (2016) and Georgia (2019)_

|Mode of Employment|Share of employed (all ages)<br>Jordan|Georgia|
|---|---|---|
|1. Farmer(owns a farm/self-employed)|0.4|35.5|
|2. Business owner/self-employed(but not a farmer)|14.4||
|3. Unpaid familyworker on a farm|1.2|22.7|
|4. Unpaid familyworker(but not a farmer)|0.4||
|5. Wage worker for Government/ public enterprises|29.6|17.2|
|6. Wage Worker for theprivate sector|54.1|24.6|
|Formal and regular|22.1|-|
|Permanent<sup>1/</sup>|21.7|11.0|
|Formal irregular or temporary|0.7|0.5|
|Informal regular orpermanent|23.2|11.0|
|Informal Irregular or temporary|8.1|2.1|

Source: Jordan Labor Market Panel Survey (LMPS) 2016 and Georgia Labor Force Survey (GLFS) 2019. Notes: GLFS 2019 does not distinguish between owner-operation of or unpaid family work on an on-farm versus an off-farm business Since GLFS 2019 does not
**Table A1: Effects of a Workerʹs Unexpected Illness on the Labor Supply of Other Household Members in the Daily Labor Supply Data**

|Dependent variable|1(Work)|Hours Worked|
|---|---|---|
|Other worker ill|‐0.024|‐0.147|
||[0.035]|[0.343]|
|Other worker ill X Female|‐0.001|‐0.065|
||[0.041]|[0.443]|
|Mean Dependent Variable|0.538|4.309|
|Observations|8,409|8,409|
|R‐squared|0.417||

_Notes: Other worker ill = 1 if another household member reported missing work for a day in which s/he was planning to work and the reason for missing work was injury/illness. Sample includes respondents employed at baseline. All specifications include individual fixed effects and gender interacted with day‐of‐the‐week dummies and week‐of ‐interview dummies, week‐of interview dummies interacted with city, a dummy for whether the report was for the day before yesterday interacted with gender, and a control for the number of household members planning to work in that round interacted with gender. Cape Coast dropped due to a small number of respondents in the daily sample. Column 1 is estimated by OLS and column 2 is estimated using least squares with trimming to account for censoring of the dependent variable (Honore, 1992). Standard errors in brackets, clustered at the household level: *** p<0.01, ** p<0.05, * p<0.1._
# **Appendix 2. The MAMS Database**

The database with this disaggregation consists of a Social Accounting Matrix (SAM), data on stocks (of factors of production and debts), elasticities (in production, consumption, trade, and MDG functions), and miscellaneous other data.<sup>19</sup> Table A2.1 shows the disaggregation of the database. The SAM, which was specifically built for this analysis, is mainly based on the 2008 Supply and Use Tables (SUTs, which also include employment data), IMF fiscal and balance of payments data, as well as disaggregated fiscal and foreign aid data from the Ministry of Finance on the allocation of foreign aid across different areas (LISGIS 2011; Ministry of Finance, 2009, pp. 27-30; Ministry of Finance, 2010, p. 8). WDI (2011) provided additional indicators, both on Liberia and across countries. Relative to earlier national accounts data (which mostly relied on extrapolations of pre-war surveys), the SUTs offer improved coverage of non-tradable domestic economic activities (including subsistence farming and informal service sectors). At the same time, the SUTs are by construction incomplete when it comes to fiscal and foreign transactions, requiring reliance on data from other sources; in this case the authors turned to IMF data. Inconsistencies between IMF and SUT data were resolved using statistical procedures. According to the resulting SAM, 2009 GDP was US$1,421 million, in contrast with an IMF figure of US$865 million for the same period and a SUT figure of US$2,025 million for calendar year 2008 (IMF 2011, p. 16; LISGIS 2011, p. i).<sup>20</sup> The analysis addresses the effects of alternative government spending patterns. In MAMS, these effects are covered via two mechanisms: (1) total factor productivity in selected production sectors is a function of the level of government service-specific capital stocks (cf. the disaggregation of
Our empirical analysis will focus on private sector dynamics over the business cycle. We will also examine the impact of private sector activity following the government trade, fiscal, and monetary interventions.

# **4. Data and summary statistics**

The data set is comprised of 5,941 unique firms in Cote d'Ivoire from 1976 through 1997, which covers the complete census of formal, private sector firms in the country.<sup>10</sup> The data was handcollected from paper filings at the "Registrar of Companies for the Modern Enterprise Sector", and does not include a sample bias of exited firms. These firms are required to pay the full range of taxes, and may benefit from bank finance and state technical assistance.

This data set appears to be consistent with the estimated overall private sector in Cote d'Ivoire, which includes, on average, about 800 large, formally registered enterprises (with at least 50 employees), several thousand formally and informally operating medium-sized enterprises (10-50 employees), and several hundred thousand informal microentrepreneurs, which mostly generate self-employment (Reuda-Sabater and Stone, 1992) **.** The structure of the data is an unbalanced panel of about 37,000 firm/year observations. That is, there is detailed information tracking firms over time and the data set includes firms that enter over the course of the sample period, as well as firms that exit during the sample. The variables of interest are information on sector classification at the two-digit sector level and firm ownership, collected at the time of registration, and annual data on firm employment, collected from required annual filings.<sup>11</sup> This data provides the first illustration of firm dynamics in a lowincome country, across sectors and time.<sup>12</sup> This includes entry and exit rates, as well as job creation and destruction rates at new versus incumbent firms.

> 10 The database excludes the primary sectors and state-owned firms.

> 11
According to Equation (8), using a predicted measure of crop cut, we will be estimating _β_<sup>_∗_</sup> with an attenuation bias coming from the noise of the ML algorithm which will also contribute to attenuate the bias induced by the SR measure of yield.

# **4.2 Empirical application**

To test empirically whether it is judicious to use machine learning techniques to construct an alternate measure of crop yields based on a sub-sample of plot observations for which both crop cut and selfreported crop production data are available, we use the data from the ERIVaS 2017 experiment, which allows us to compare ML-predicted crop cut yields to their observed counterparts. Using data from a more controlled methodological study also ensures that the crop cut data are of the highest quality. A potential disadvantage, on the other hand, is that because the data are from a localized setting, it may be less heterogeneous, which could make it harder to accurately predict each observation. Our experimental protocol is as follows. First, we randomly select one-third of the observations in each ERIVaS village to construct a pooled data set to train the ML algorithm. The reason why we choose one-third of the observations to be part of the training data set is to mimic the extent of crop cutting conducted in the nationally-representative multi-topic household surveys that have been supported by the World Bank LSMS-ISA program. The remaining two-thirds of the data set serves as our test sample in which we compare the predicted yield measures obtained with ML against those based on farmer self-reporting and crop cutting, with the latter assumed to be closest to the “truth”. The statistics tied to this comparative assessment are computed over 100 simulations of training and test sample splits to ensure that are conclusions are not the result of one random realization, although in some parts of this subsection, we may present the results for one realization for better illustration purposes. Second, since our goal is to investigate whether the ML-based predicted crop yields are suitable for the analysis of
type of benefit.

However, to strengthen competition in the market for variable payout annuities, the central register should also compile comparative data on a consistent and informative basis on the performance and bonus policies of different providers. This will enhance the transparency of the system. The register should focus on key aspects of pricing policies and performance, such as profit-sharing rules, levels of operating costs and fees, commission fees and surrender charges, and consistency and soundness of investment policies rather than on a mere reporting of past performance data. At the same time, annuitants should be encouraged to select providers with diversified investment portfolios, a consistent record of sound performance, low operating fees, and high profit participation rates. Focusing on recent past performance, which is often underscored by selling agents, is not sound practice since past returns are not good predictors of future performance.

A central register along these lines is easier to operate for participating policies where the number of factors that need to be followed is relatively small. It is a much greater challenge in the case of US-style variable annuities with their wide range of guarantees. In recent years various websites have been created that compile such data. What is needed is effective regulatory 27
the program schools, in 2000 or 2001. Non-transfers are school participants who did not transfer in 2000 or 2001.

transferred. Hence it is unlikely that transfers between schools bias the estimation results when the estimation is con...ned to the sample of baseline children.

The third class of transfers is hidden transfers. A hidden transfer is the enrollment of a baseline child in a non-program school, when that child has never attended a program school. This child would be counted as absent in my school participation data. If the feeding program a¤ected the rate at which baseline children enroll in a non-program school, the estimates will be biased. The bias in the estimate of the school participation e¤ect will be upward if children in the baselines of treatment schools were less likely to enroll in a non-program school than children in the baselines of treatment schools. To try to get at this problem, retrospective data were collected on the whereabouts of baseline children. In 2002, 40 households from each school were sampled and the NGO sta¤ tried to identify and interview one child from each household. The child was asked about the whereabouts of the household’s children who were recorded in the baseline. This information was not yet available at the time of writing.

# 5 Econometric modeling and results

We estimate the impact of the program on the average school participation rate of a child, on the probability of ...nding a child in school at least once, on height and weight and on teacher absenteeism. In section 6, we estimate the impact of the program on the price of preschool.

19
31 Table S. 8: Within-cohort weight gain regressions using National Family Health Survey data

||(1)|(2)|(3)|(4)|(5)|(6)|(7)|(8)|
|---|---|---|---|---|---|---|---|---|
|VARIABLES|Men|Women|Ev. m. wom.|Wom. ex.1998|Men|Women|Ev. m. wom.|Wom. ex.1998|
|year|0.613***|0.549***|0.538***|0.607***|0.682***|0.536***|0.521***|0.608***|
||(0.009)|(0.003)|(0.003)|(0.004)|(0.008)|(0.003)|(0.003)|(0.004)|
|Cohort FE|Yes|Yes|Yes|Yes|Yes|Yes|Yes|Yes|
|Height control|No|No|No|No|Yes|Yes|Yes|Yes|
|Observations|180,794|892,509|694,387|811,964|180,794|892,509|694,387|811,964|
|R-squared|0.154|0.159|0.140|0.158|0.331|0.257|0.237|0.264|

_Source_ : Authors’ calculations using National Family Health Survey rounds 2, 3, and 4. _Note_ : Robust standard errors in parentheses. *** p _<_ 0.01, ** p _
# **1 Introduction**

The COVID-19 pandemic has disrupted the daily operations of firms on a staggering scale. Sharp, short-term impacts of the pandemic on firms have been reported across geographies and sectors, with some of the largest effects concentrated in small and medium-sized firms (Apedo-Amah et al., 2020),the back-bone of many developing economies. Using a novel data set collected by the World Bank Group and several partner institutions in 51 countries covering more than 100,000 businesses, their work provides a comprehensive assessment of the short-term impact of the COVID-19 pandemic on businesses across the world-wide with a focus on developing countries. Their findings provide a better understanding of the magnitude and distribution of the shock, the main channels affecting businesses, and how firms are adjusting. While their findings suggest that the distribution of impacts across firms within nations, sectors, and size categories varies widely, and that organization-level factors may be driving these outcomes, they do not offer insights on the mechanisms through which firms become more resilient. This paper explores one such channel, that is, whether structured management practices can help firms cope with the COVID-19 crisis.

A large literature finds that structured management practices are strongly related to firm performance, explaining one-fifth of the variation in total factor productivity across firms within countries (Bloom et al., 2016). This positive association extends to environmental stewardship (Bloom et al., 2010), fair labor practices (Distelhorst et al., 2017), and health or educational outcomes in hospitals and schools, respectively (Bloom et al., 2013, 2015). Given the important and multifaceted role of structured management practices in long-run firm outcomes, it is of interest to understand if these disciplines also help firms adjust in the face of shocks such as the COVID-19 pandemic. On the one hand, highly structured practices could lead to less nimble decision-making, if they require analysis of (not yet available) data or reaching consensus; or, these practices could lead to more sound decisions in crisis
Ten years of data on the presence of surfactants (compounds used in cleaning products such as laundry soap) in the Warta at Poznań found declines in anionic surfactants but a substantial (six-fold) increase in nonanionic surfactants during the 1990s. Some of this change reflected the changing ratio of these compounds in cleaning products themselves, but that alone would not account for the size of the increase in nonanionic surfactants. Data from measuring stations at other points along the river indicated that concentrations of nonanionic surfactants were highest downstream of the industrial cities of Czestochowa, Sieradz, and Gorzow. High concentrations were also found on the Ner river downstream of Łódź, where the anionic surfactant concentrations “were more similar to those of a raw sewage than surface water.” (Szymanski et al. 2001: 375) Fish populations, species diversity, and size and weight provide indirect indicators of water quality improvement or decline. For the most part, data on fish in the Warta River indicate that water quality still needs considerable improvement. In the farthest upper reaches of the Warta river, where water quality is significantly impaired by mining and other industrial discharges, no fish are found. (Przybylski 1996: 40) The next river portion—the stretch of the upper Warta between the Poraj and the Jeziorsko reservoirs, along which the riverbed and banks are natural—exhibits eutrophication problems but the highest fish population, growth rates, sizes, and species diversity. (Przybylski 1996; Penczak et al. 1998) Both species richness and species diversity in the Warta drop dramatically along the stretch of the Warta from the Ner mouth to the Prosna mouth. (Przybylski 1993: 508) “Drastic changes in the fish fauna composition were noted in the polluted parts of the upper Warta, especially in the part above the inflow of the Ner River (pollution from the city of Łódź, via the Ner). In this part of the river, the species number is reduced to a minimum value of four.” (Przybylski 1993: 506) Along the middle
14 analysis and neural networks to measure efficiency in the Italian banking industry). However, the data required for this type of analysis are available only for a sub-set of countries.

For financial markets, efficiency measures focus less on directly measuring the cost of transactions and more on measuring transactions. A basic measure of efficiency in the stock market is the turnover ratio, i.e. the ratio of turnover to capitalization in the stock market. The logic of using this variable is that the higher the turnover (the more liquidity), the more efficient the market. In the bond market, the most commonly used variable is the tightness of the bid-ask spread (with the U.S. and Western European markets showing low spreads, and Vietnam, Peru, Qatar, Dominican Republic, and Pakistan reporting high spreads) and the turnover ratio (although the measurement of the latter often suffers from incomplete data).

A range of other proxies for efficiency in financial markets has been used in empirical literature (Table 1). One of them is price synchronicity, calculated as a degree of co-movement of individual stock returns in an equity market. The variable aims to capture the information content of daily stock prices, as a market operates efficiently only when prices are informative about the performance of individual firms. Another proxy variable for efficiency is private information trading, defined as the percentage of firms with trading patterns that arise from trading conducted through privately obtained information. This calculation is based on the examination of daily price-volume patterns, and helps indicate the prevalence of trading in a stock based on private or privileged information. Finally, efficiency can be approximated by the real transaction cost. Based on daily return data of the listed stocks, this variable attempts to approximate the transaction costs associated with trading a particular security. This variable helps determine the barriers to efficiency in the market. All these indicators are constructed by compiling and statistically processing firm-level data from a variety of market sources.

# _3.4. Financial Stability_

Last, but not
- BASE: Moderate growth; variables follow their historical trend; no additional investment or productivity gain.

- INF-FL: Front-loading of additional infrastructure investments (CFAF 2,000 billion in 2013-2015); no productivity gain.

- INF-BL: Back-loading of additional infrastructure investment (CFAF 2,000 billion in 2016-2022); no productivity gain.

- INF-BL(15), INF-BL(30), INF-BL(45): Delayed additional infrastructure investment similar to INFBL with an annual investment productivity gain of 1.5 percent, 3.0 percent and 4.5 percent, respectively.

- GOVEFF-INF: Improved government efficiency; gains are used to scale up government services for infrastructure.

- GOVEFF-HE: Improved government efficiency; gains are used to scale up government services for health and education.

- GOVEFF- HE+INF: Improved government efficiency; gains are used to scale up government services for health, education and infrastructure.

# 4.1 <u>Baseline scenario</u>

Under the baseline scenario (BASE), it is assumed that growth remains moderate with on average 5.6 percent per year between 2010 and 2025 and with most variables following their historic paths. The base year of the model is 2009, but all available information and estimates for 2010 and 2011 have been taken into account, especially regarding the government budget.

**Table 5:** Real macro indicators by simulation (average annual growth from 2010 to 2025, percent)

|||Front-loading<br>|B<br>|ack-loa<br>|ding (B<br>|L)<br>|Increase<br>|d government<br>|efficiency<br>|
|---|---|---|---|---|---|---|---|---|---|
||BASE|INF-FL|0|15|30|45|HD|HD-INF|
under the MoRA, including: number of students (data available by grade depending on the published year), number and condition (good, mild-/medium-/heavy- damaged) of school facilities such as classrooms, library, laboratories (computer, science, language), ownership status (public/private), and official instructional hours. Data reliability of Dapodik and EMIS is still an issue due to large variation in school operators’ competency in managing the data system and the lack of a verification mechanism. Nevertheless, local governments use Dapodik and EMIS in their planning documents as well, because they are the only administrative data available at the school level.

Based on a desk review, districts were identified for a deeper dive to conduct the qualitative analysis to better understand gender gaps in education. Districts were selected based on existing gender gaps in enrollment at different levels of education, coupled with data on adult literacy rates, school completion rates and reports on child marriage. Field visits were conducted to selected districts in two provinces to uncover drivers of gender differences, as well as issues of social inclusion, and to explore policy approaches to improve learning outcomes and educational achievement for all children. Interviews were held with local government education officers, MoRA provincial-level and district-level education officers, and school and _madrasah_ principals. During school and _madrasah_ visits, focus group discussions were held with supervisors, teachers, parents and students. Interviews were also held with several MoEC and MoRA officials in the central government, as well as an official from the Ministry of Women’s Empowerment and Child Protection (MoWECP).

There were some limitations that impacted the district selection for field visits. A number of provinces were off limits due to natural disasters, such as the earthquake and tsunami in Sulawesi, and areas that were difficult to reach, such as Nusa Tenggara and Papua. With these constraints in mind, the provinces of East Java and South Kalimantan were selected.

5
Bottom 40, the Top 60 and the overall population between 2011 and 2018.<sup>14</sup> The trend mirrors the evolution of the total income, with strong annual real growth in the first half of the decade for the Bottom 40 (8.2%) and the Top 60 (4.6%). This strong growth came to a halt between 2016 and 2018, a period that marked the exchange rate volatility crisis and the rapid increase in inflation. Real incomes posted negative growth rates, on average -0.8% for the Bottom 40 and -2% for the rest of the population. The labor income results show that the drop in overall income growth for the bottom of the distribution was driven by stagnant employment related to real income levels after 2016.

Figure 8: Real incomes of 1st and 10th decile (2011-2018, 2011 =100) <!-- Start of picture text --> 150 18<br>16<br>140<br>14<br>130 12<br>10<br>120<br>8<br>110 6<br>4<br>100<br>2<br>90 0<br>2011 2012 2013 2014 2015 2016 2017 2018<br>1st Decile 10th Decile CPI<br>Real Income<br>Annual CPI Change (%)<br><!-- End of picture text --> _Source_ : World Bank calculations using data from TUIK Figure 9: Real labor income of the Bottom 40 and Top 60 (2011-2018, 2011 =100) <!-- Start of picture text --> 160<br>148 147 146<br>150<br>140 134<br>130<br>120<br>117<br>120<br>125 125<br>107 121 120<br>110<br>113<br>100 107<br>104<br