rafmacalaba/data-use-mentions-extended huggingface dataset:
latest year available. Modeled ILO LFP. The modeled ILO comparable number for FLFP in Nepal is 29 percent in 2016, an increase from 22 percent in 2003.

3 Source: Nepal Labor Force Survey 2017/2018. The labor force participation rate is a measure of the proportion of a country's working-age population (individuals 15 years and older) that engages actively in the labor market, either by working or looking for work. Following the 19<sup>th</sup> International Conference of Labour Statisticians in 2013 – the new standards that Nepal adopted for the Labor Force Survey in 2017/18 – labor force participation includes work performed for pay, profit, or family income, and excludes subsistence work (production for own final use), whether in agriculture or in non-agriculture family or household enterprises. In past publications (e.g., Ruppert Bulmer et al., 2020), women’s labor force participation was estimated at 75 percent using the 2017/2018 Labor Force Survey as subsistence activities were counted in the definition of work.

4 Over the last three decades, women achieved strong education gains. Average years of schooling rose from 2 years for females born in 1960 to over 9 years for those born after 1990. A woman with a college degree, on average, earns double what a woman with incomplete primary earns, controlling for sector of work and other factors (Ruppert Bulmer et al. 2020).

> 5 Nepal Labor Force Survey 2017/2018.

2
<!-- Start of picture text --> All beneficiaries in 6 communes enrolled in<br>Social or Complete Productive Packages of<br>the Sahel Adaptive Social Protection<br>Program in Niger (n=2,628)<br>Group-level randomization to 25%, 50%, or<br>75% saturation of individual-level treatment<br>(n=108 groups)<br>iainnapeeeane) to control PersonalIndividual-levelInitiath randomization toition (n=666)<br>+ Received intervention (n=642, 96%)<br>Follow-up survey at 1 year (n=1,281) Follow-up survey at 1 year (n=626) Follow-up survey at 1 year (n=645)<br>+ Lost to follow-up (n=74, 6%) + Lostto follow-up (n=40, 6%) + Lost to follow-up (n=21, 3%)<br><!-- End of picture text -->
” score using the Infrastructure SOE Database and show that it can be used to forecast the need for fiscal injections in SOEs. Our estimates reinforce the idea that ex ante monitoring of SOE financials can significantly reduce the need for fiscal injections and thus reduce overall fiscal risk in a country.

The paper is organized as following. Section 2 defines a typology of fiscal risks. Section 3 describes the data. Section 4 defines the fiscal injection variables and presents the descriptive analysis of fiscal injections. Section 5 presents the methodology and findings on predicting fiscal risks. Section 6 summarizes the paper’s main conclusions.

> 2 Using gross figures to analyze fiscal risk may overestimate the actual fiscal cost of a bailout (from a recapitalization and/or substitution of liabilities, for example) and confuse bailouts/fiscal risk with transfers to provide productive assets to an SOE, a common practice around the world. Therefore, in contrast to existing approaches to measure fiscal risk, we examine fiscal injections net of changes in assets. See the definitions in section 4.

> 3 Our measures of fiscal injection capture the explicit fiscal costs of SOEs. They differ from the measure of quasifiscal deficit used by Huenteler et al. (2020), who measure the implicit or potential fiscal cost as “the difference between the cash collected by the existing utility and the revenues that would be collected without bill collection losses by a utility applying cost-recovery tariffs . . . and achieving commercial and operational efficiency.”

4
nonlinearities. This is a comparison of non-nested models with an unknown, possibly realvalued, difference in degrees of freedom.<sup>8</sup> To decide on the right number of effects, we start by estimating a model that includes all fixed effects. We then remove the least significant dummy, and obtain new results. We repeatedly evaluate the out-of-sample prediction performance while shrinking the effects, and select the model with the optimal out-of-sample density across all fixed effect models. Intuitively, this approach starts with a model similar to a linear Fixed Effects model, as the penalty heavily discounts the thresholds introduced by the effects resulting in flattened marginal effects, and gradually allows fixed heterogeneity to be explained by nonlinearities across covariates instead. As a result, our final estimates are guaranteed to be preferred over the standard linear Fixed Effects model, as judged by the out-of-sample criterion.

# **3 Data**

We combined measures of tree loss, air pollution concentrations and carbon emissions on one side, and GDP indicators of economic structure on the other. Our data are from a variety of sources and includes 95 countries measured over 1999 _−_ 2014 containing approximately 85% of the world’s population, 83% of the world’s carbon output, and 72% of the world’s forest cover. The selection is mainly driven by availability of data as detailed in the following sub-sections. We have removed areas below 1500 square kilometers _−_ essentially all small islands _−_ from the analysis. A summary of the data as it enters our regressions is given below. A list of all countries included in our sample can be found in fig. 1.

Table 1 summarizes the data, all predictors are mapped into the [0 _,_ 1] interval to ensure the penalization effect is not driven by differences in variance of the different variables. We scale back the estimation results for easier interpretation.

## **3.1 Forest cover data**

We use data from (Hansen
inefficient and which calls for budgetary provisions for particular goods. But this insight tells us nothing about the desirability, if any, of public production and, hence, of the need for public employment.1 An alternative approach to evaluating government pay and employment is to take as given those goods and services chosen for public production and to consider whether such production is efficient in terms of input use and unit costs. Such evaluations, however, face their own constraints. For one, since the government is often a monopoly supplier, there may be no basis upon which to compare the efficiency of its production. Second, the non-market character of most government services precludes the use of market tests of efficiency. Government productivity data, based on input costs as a measure of output, reflect this problem. Third, the non-tradeable character of most government production suggests that border prices are unavailable as points of reference. Unlike the cases of, say, agricultural price distortions, one cannot turn to world prices as an index of excessive or insufficient pay for government workers. Even domestic private sector wages are an imperfect standard. Given the magnitude of government as a wage employer, private wages are never independent of a government's own wage and employment decisions. Furthermore, the relative security of public jobs imparts a compensating differential problem precluding any simple comparison of public versus private pay.

The analysis of appropriate government pay and employment policies is further handicapped by the multiplicity of objectives which guide such policies. Cost minimization of iublicly produced goods and services competes with independent employment and wage objectives which may serve political or ideological purposes. In addition, while the level and structure of pay and employment influence government performance, management practices and 4
, S.N., Hillen, M.M., Nicholls, R.J., Kanning, W., van Ledden, M. (2013) Costs of Adapting Coastal Defences to Sea-Level Rise— New Estimates and Their Implications. Journal of Coastal Research 1212–1226. doi:10.2112/JCOASTRES-D-12-00230.1 Kaneko, S., Toyota, T. (2011) Long-term urbanization and land subsidence in Asian megacities: an indicators system approach. In: M. Taniguchi (ed.), Groundwater and Subsurface Environments: Human Impacts in Asian Coastal Cities, DOI 10.1007/978-4-431-53904-9_13, pp. 249–270.

Lavery, S. & Donovan, B. (2005) Flood risk management in the Thames Estuary looking ahead 100 years. _Philosophical Transactions of the Royal Society A: Mathematical, Physical and Engineering Sciences_ **363** , 1455-1474. (DOI:10.1098/rsta.2005.1579).

Lincke, D. and Hinkel, J. (2018) Economically robust protection against 21<sup>st</sup> century sealevel rise. Global Environmental Change, accepted.

Lowe, J.A., Howard, T.P., Pardaens, A., Tinker, J., Holt, J., Wakelin, S., Milne, G., Leake, J., Wolf, J., Horsburgh, K., Reeder, T., Jenkins, G., Ridley, J., Dye, S., Bradley, S. (2009) Marine and coastal projections, in: UK Climate Projections Science Report. Met Office Hadley Centre and Exeter, UK.

Menéndez, M., Woodworth, P.L. (2010) Changes in extreme high water levels based on a quasi-global tide-gauge data set. Journal of Geophysical Research 115. doi:10.1029/2009JC005997 Mooyaart, L.F. and Jonkman, S.N. (2017) Overview and Design
VA net SSC. All the other components are calculated by applying Households’ income shares ( _σhh_ from DF estimation, see Table 1) to the capital income of households from the national macro SAM ( _KYhh_<sup>_macroSAM_</sup> ). The macro SAM is a SAM that includes only macro aggregates. The shares _σhh_ represent the income source for households across the regions (e.g., 7% of income from capital goes to families in _Piemonte_ ). As an example, the household’s capital income _KY_ is the following:

- Enterprises income is the sum of enterprises’ capital income (€342.2 billion) and interest paid to enterprises (€239.2 billion). The first is the enterprises’ capital income from macro SAM redistributed on Gross Operating Surplus (GOS). The GOS is equal to the sum of value added over the regions minus compensation of labor, social security contributions (SSC), taxes on production, business tax (IRAP), and corporate income tax (CIT or IRES). The second, interest to enterprises, is obtained as in Equation 8;

- Government income is the sum of depreciation, SSC by employer, personal income tax (PIT) , taxes on production, CIT, taxes on commodities, value added tax (VAT), excises, import tax, and interest payments to the Government. The government capital income is reconstructed from the national macro SAM.

- Local Governments’ income is the sum of property tax (IMU), regional PIT, municipal PIT, and IRAP.

# **3.5 National Taxes**

In our regional SAM, the Italian national taxes are disaggregated as follows:

- PIT at national level net tax for the tax year 2016. The aggregation is conducted on regions and the total national amount is equal to €238 billion;

- Production taxes redistributed on value added plus IRAP paid by public administrations. The production taxes are the sum of other taxes on production and negative
relative ES-implied beta generates a highly significant return of 0.5% per month. Results from the Fama-MacBeth regressions show that the relative ES-implied beta implies a highly significant risk premium of 0.6% when controlling for the CAPM beta and remains significant after controlling for other variables.

A series of robustness checks is conducted to confirm that the predictive power of the relative ES-implied beta is not explained by other variables, nor does it depend on empirical settings. First, I examine the predictive power of the relative ES-implied beta and the relative usual downside beta after controlling for each other. While the relative usual downside beta only generates a significant spread within some quintiles sorted by the relative ES-implied beta, the relative ES-implied beta generates a significant spread within all quintiles sorted by the relative usual downside beta. I then examine the preditive power of the relative ES-implied beta and the relative usual downside beta after controlling for coskewness. Although the relative usual downside beta only generates a significant spread within stocks with low coskewness, the relative ES-implied beta generates a significant spread within all coskewness-sort quintiles. I also examine the predictive power of the relative ES-implied beta over future returns after controlling for size, cokurtosis and volatility, which are likely to capture the predictive power of the relative ES-implied beta. Results of double sorting portfolios indicate that risk premium on the relative ES-implied beta is overall prevalent among all stocks, although the premium appears to be stronger for small-cap stocks and stocks with low cokurtosis and high volatility. Last but not least, I change several empirical settings in the main analysis. The results suggest that the predictive power of the relative ES-implied beta is not driven by the sample nor the breakpoints used in the main analysis.

18
2.0 -1.0 0.0 1.0 2.0<br>s = -0.77<br>-0.5<br>-1.5<br>-1.0 -2.0<br>Philippines STEP survey Philippines STEP survey<br>Source: Authors’ elaboration based on 2015-2016 Philippines STEP survey and O*Net database.<br>Note: s indicates the Spearman’s rank correlation coefficient between skills measures.<br>U.S. O*Net database U.S. O*Net database U.S. O*Net database<br>U.S. O*Net database U.S. O*Net database<br><!-- End of picture text --> 58
Barriers to Entry, we choose the “Dealing with Construction Permits” score.

In addition, a relevant indicator outside the scope of _Doing Business_ that comes from the World Bank is the newly minted measure of Human Capital (3), the Harmonized Learning Outcome (HLO), a country-level index for education quality (for more details, see Angrist et al. 2019). A higher value of a _Doing Business_ (and HLO) indicator implies a better BE. Table 3 reports the summary statistics of all the BE indicators from various sources. Because the scale of these indicators differs greatly, to facilitate comparison, we standardize them into variables with means of zero and variances of one (that is, the z-score) in our regression analysis.

These three groups of indicators are different and complementary. First, all the _Doing Business_ indicators and the Global Competitiveness indicators are at the country level and

> 7 The _Doing Business_ project does not capture some BE aspects by design, including the quality of the labor force, incidence of bribery and corruption, and lack of security. See table 2.2 of the most recent _Doing Business_ report (World Bank 2019).

10
6 not on constructing highly accurate rainfall maps as such, necessary for example for the design of weather index insurance programs, but rather on the household welfare effects of weather related events, a pragmatic approach has been pursued here, aimed at minimizing measurement error of the disaggregated rainfall measures at a reasonable cost, while maintaining maximum replicability of the approach in other settings.

In particular, using the historical daily rainfall records over 1980‐2006 in 166 geo‐referenced weather stations from across Vietnam (see Figure 1)<sup>9</sup> , monthly rainfall grids of 0.1 degree resolution are constructed<sup>10</sup> using both inverse distance weighting (IDW) and inverse elevation difference weighting (IEW). In IDW, one of the most widely used spatial interpolation techniques in practice (Tomczak, 2003), the rainfall value in a particular grid cell is obtained as the weighted average of the rainfall data from a predetermined set of neighboring weather stations (e.g. the 5 closest or all the neighboring stations within a certain distance) using the inverse distance between the station and the center of the grid cell (raised to a power) as weight and normalized by the sum of the weights.<sup>11</sup> The squared inverse distance to the five nearest stations for which data were available was chosen in the empirical application below. IDW exploits the intuition that the further away the station is, the less its rainfall pattern will resemble this at the point of interest. However, even over short distances rainfall patterns can still diverge widely, especially among locations at different altitudes (Hutchinson, 2003). Given its mountainous range, high slope gradients are frequently observed in Vietnam, with some grids displaying within grid elevation differences of more than 1,000 meters. To exploit this largely neglected insight (Dubois and Shilbi, 2003), an elevation difference weighted rainfall index based on the ten nearest stations is also constructed.<sup>12</
shared, neither party to the contract can reap the full marginal benefits from such investment. Thus, unless parties reach an implicit and enforceable agreement on how to share benefits, investment levels will be below what would be expected under full ownership. The long-term nature of contractual relationships in West Bengal implies that any effects will be lower than what one would expect in a setting where sharecropping contracts are renewed annually and contractual parameters are less constrained.

# **2.4 Estimation strategy**

To quantify the effect of sharecropping on agricultural production revenues and the probability of making long-term land-attached investments, we follow the literature (Bell 1977; Shaban 1987) and use plot level data on production and investment from our sample of owner-cum tenants to estimate 6
using 6‐digit HS product codes (1992 revision). Using Atlas trade data requires matching HS 1992 to ISIC revision 4, using the relevant correspondence table provided on the World Trade Integrated Trade Solutions website.<sup>6</sup> This table matches the 6‐digit HS (revision 1992) codes to ISIC (revision 2) 4‐digit codes. We then move from revision 2 to revision 4 of the ISIC.

The second source of data is the population census data. The 2000 population census data (undertaken 3 years before the 2003 NIC) was obtained from IPUMS, which gives a number of other characteristics of each district, including measures of the overall size of the potential market locally (proxied by the total number of individuals in wage and self‐employment in each district) and measures of human capital in each district. The data also includes the size of each district, which we use to compute employment density, an urbanization economy that may contribute to local employment growth. We can also use the IPUMS population census data to obtain spatial information on the distance of the centroid of each district from both Kumasi and Accra, which we believe are reasonable measures of transport costs to large markets and (for Accra) export markets. This data is created using the GIS information contained in the IPUMS release of the 2000 population census.

We also have district‐level population census data for 1984 and 1970. These are based on the census microdata but are not publicly available. They were obtained from Lasse Møller‐Jensen at the University of Copenhagen and were used in Møller‐Jensen and Knudsen (2008).

# 4 Descriptive Analysis

Before investigating the determinants of local growth in Ghana we describe employment growth in Ghana, the distribution of the localization and urbanization measures that we use in our growth regression analysis and the population and trade data.

## 4.1 Ghanaian firm census data

### **Firm level data**

Table 1 shows the employment and firm numbers and
and a rate of 81 percent is reported for Angola, a relatively richer country.<sup>1</sup> Even for countries where micro data from household surveys are widely available, the methods of questionnaire design and implementation generate indicators which fail to capture what the survey intended to measure (non-sampling errors), and are not comparable over time. Guarcello et al (2010) found this in the case of child labor statistics in low and middle-income countries. They noted substantial differences between indicators of child LFPR across similar countries, and even within the same country over a short period of time. They contrasted the variance in the measurement of child LFPR with measures of school attendance, and found that the latter did not show such high variance depending on type of survey. They attributed the unusual variance in point estimates of child LFPR to differences in the framing of questions and the methods by which the employment data are collected. These differences also plague measures of adult employment outcomes. As a result, even trends within country are hard to identify, much less trends among countries.

One reason for the poor quality of data is that until recently, employment outcomes were not a primary focus of development policy, and therefore of the household surveys. The indicators most widely tracked at the household level were social services access and outcomes, and household assets, consumption and

> 1 Many of the LFPR numbers commonly reported are not data points collected recently, but projections based on old surveys. Although they are available for most countries for most years, they may not be based on recent data.

2
Among the neighboring countries, Indonesia was the first to impose lockdown measures (January 18) followed by China (January 23), Vietnam (January 25), Malaysia (January 30), and Thailand (March 6). Of these countries, only China started by implementing three internal measures concurrently: restraint of internal movements, imposition of stayat-home requirements, and closure of public transport. It also had the strictest lockdown policies in the world for most of the first quarter of 2020 (Islamaj et al., 2021). Other countries started with international travel controls and progressively introduced domestic measures. Malaysia’s stringency index was the lowest, of all neighbors of the Philippines, before November 2020 because of the delay in closing public transport. China had the highest stringency index, followed by Malaysia, Indonesia, Thailand, and Vietnam, respectively, by the end of 2020.

<!-- Start of picture text --> 100<br>80<br>60<br>40<br>China<br>Indonesia<br>20 Malaysia<br>Philippines<br>Thailand<br>Philippines' lockdown (31-Jan-2020) Vietnam<br>0<br>Date<br>Stringency Index<br>01-Jan-2020 01-Feb-2020 01-Mar-2020 01-Apr-2020 01-May-2020 01-Jun-2020 01-Jul-2020 01-Aug-2020 01-Sep-2020 01-Oct-2020 01-Nov-2020 01-Dec-2020 01-Jan-2121<br><!-- End of picture text -->

**Figure 1: COVID-19 Stringency Index of the Philippines and nearby countries in 2020**

Source: Author’s compilation using data from Hale et al. (2020) 6
dummy 2000 variables, ‘more than operational 3588 and maintenance costs’ and ‘less (55%) 1500 than operational and maintenance costs’; and 1000 _proximity to growth centers_ , 121 measured using transport costs 500 2% from the village office to the 0 subdistrict office. We measured Fully Functional for last 5 years Not Functional for last 5 years (2%) _water conditions_ in the village (55%) Functionality using presence of water pollution **Source: World Bank Team 2021**

> 8 Where ‘sufficient’ is a qualitative measure taken by project facilitators through household interviews.

> 9 The project allows one to identify if any rehabilitation funds have been received by the village. In many villages where water systems are not fully functional, it is common for PAMSIMAS to provide rehabilitation funds to enable villages to move from partially or not functional to fully functional status.

> 10 The management information system (MIS) data are likely to be overreported in terms of functionality as the reporting system relied on facilitators who might have the incentives to report positively on scheme conditions, since it reflects upon their work to some degree. To mitigate this effect, we cross-checked the data and made spot calls to a sample of districtand village-level project facilitators. Despite the presence of some outliers, that were discarded and a handful of cases only, the data remained consistent, logical, and within an expected range. For example, the median for training participation and proposal meeting was around 34 to 37. Around 250 of 6,507 villages turned out to have data mistakes and these cases were removed from the analysis.

11 These meetings are meant to introduce the project, raise community awareness of WASH, generate cash and in-kind contributions, and elect user group leaders for post-construction management.

7