rafmacalaba/data-use-mentions-extended huggingface dataset:
Nevertheless at the extensive margin, the decision whether or not to take a job, the elasticity of labor supply is positive. For our developing countries, we felt that some small responsiveness of labor supply to changes in taxation was appropriate, and thus chose an elasticity of 0.05, implying that _T_ = 1 _._ 05 _× L_ .

National accounts figures represent values: price _×_ quantity. Following the Harberger convention, units of aggregate goods are chosen such that quantities equal values. This implies that for untaxed goods or factors, benchmark prices are equal to one. For taxed goods and factors, we choose quantity units such that either the net of tax price or the gross of tax price is equal to one at the benchmark equilibrium. Specification of the tax rates allows calculation of the missing prices.

With all benchmark prices and quantities determined, calibration finishes with the selection of elasticities for the CES utility and production functions. Our base case uses unitary elasticities: constant returns to scale Cobb-Douglas functions. This appears to be reasonable for the elasticity of demand for imports (Senhadji 1998). But we do not have evidence on the empirical magnitude of other necessary elasticities. Faced with a data vacuum we preferred a simple functional specification, and performed sensitivity checks by changing elasticities.

While this account of the calibration process is straight-forward, it glosses over several problems with the calibration of informal sectors. It is likely that these problems affect all countries to some degree, but the issue is particularly acute in eight countries where, using the procedure described above, the calibrated values of sections of the informal economy are negative. The _untaxed_ good is negative in Equatorial Guinea (-74% of GDP), Gabon (-2 %), Mauritania (-3%), Namibia (-22%), and Swaziland (-3%). In addition, the calibration process gives negative values for _informal labor_ used to produce _domestic_ and _exports_ in Cape Verde (-5%), South
reduce economic inequality (Grimes 2017; Kettl 2017; Listhaug and Jacobsen 2017; Newton, Stolle, and Zmerli 2017). Many have also found strong negative correlation between trust and corruption (Braithwaite 1998; van der Meer 2010; van der Meer and Zmerli 2017). Additionally, the use of open government policies and initiatives, e-government, and social media in the resource management process have been shown to increase trust in institutions, albeit with some caveats (see Kumagai and Ilorio 2020 for a review of that specific branch of the literature).

The non-exhaustive list of potential drivers of trust described in the previous paragraph are often categorized as belonging to two important families of drivers of institutional

> 5Looking at the Arab Barometer waves, the percentages of people expressing having a great deal or quite a lot of trust in the government stands at 39 percent in 2006, then 42 percent in 2013, 43 percent in 2016, 29 percent in 2018 and 37 percent in 2022.

6https://apnews.com/article/coronavirus-pandemic-africa-health-north-africa-morocco86e49c437629db0a51d508e94da306e1 7https://www.reuters.com/world/africa/moroccos-vaccine-pass-stumbles-with-new-variantlooming-2021-12-07/.

3
the CONAFOR remote sensing team suggest that the Hansen product offers better results in Mexico when the percentages of forest cover are below 30 or above 60 percent. The data are likely to understate loss of natural forest because they may classify plantations and agroforestry crops as forested areas, and they may also understate selective logging--an important source of forest degradation --or very small areas of deforestation. Furthermore, the data do not not capture degradation (and no forest cover change data set captures other important ecosystem services such as conservation of biodiversity, soil conservation, or watershed functions). For these reasons, our estimates are likely to be conservative with respect to forest cover change and do not measure multiple other potential environmental impacts. Most importantly, all conclusions are limited by the lack of power in the analysis. Although pre-analysis power calculation estimates suggested that we could detect medium sized effects, the actual power based on the Hansen data from the study years and the entire universe of applicants reveals substantial power limitations (further discussed below).

For the analysis of land-cover impacts, the unit of analysis for the environmental estimates is the parcel (polygon). These polygons were created by dividing applicant parcels into smaller units so that they have unique application histories and we avoid any possible issues of double-counting areas. For example, if a landowner submitted a parcel in 2011 and was rejected, and the following year submitted an imperfectly overlapping parcel that was accepted, these two applications would generate three polygons: one rejected in 2011, one rejected in 2011 and accepted in 2012, and one accepted in 2012.<sup>10</sup> We cluster the standard errors by common property to account for the spatial clustering of these units. **Figure 4** shows a visual representation of these units within various communities with repeated applications.

> 9 The high-resolution MAD-Mex system being developed by CONAFOR was originally the preferred option, but postprocessing of this layer was not completed by the time of analysis.

> 10 A preliminary analysis of
**Figure 1: The RUSMOD policy spine (order of simulations in the model)**

|Net market income (formal and informal earnings, investment incomes, private transfers and income from<br>subsistence farming)|
|---|
|+Simulated Employer andSelf-employed social insurance contributions(SIC)|
|+Simulated Personal income tax(PIT),incl. standard tax allowances|
|=Gross market income|
|-Simulated Employer andSelf-employedSIC|
|- Direct taxes(simulated PIT,not simulatedSocial tax allowance for thepreviousyear, property &vehicle taxes)|
|+ Not simulated benefits (public pensions, unemployment benefit, unified cash payment, housing privileges,<br>scholarships)|
|+ Simulated not means-tested benefits (childbirth grant, maternity allowance, childcare allowance up to 1.5 years,<br>compensation of childcare costs toparents,maternitycapital)|
|+ Simulated means-tested benefits (child allowance up to 16(18) years, allowance for the third child up to 3 years,<br>state social assistance,housingsubsidy)|
|=Disposable income|
|-Value added tax(VAT)|
|- Excise duties on alcohol,tobacco and car fuel|
|=Consumable or Post-Fiscal income|

The output of the model has been validated against official statistics (national accounts and administrative data) to assess the robustness of the model and its applicability for policy analysis, following the EUROMOD standards. To ensure comparability with the external sources some assumptions were used in the model. In general, although our modeling reflects as close as possible the existing statutory rules, in the baseline scenario we opted for computing economic rather than statutory incidence of taxes and benefits. Overall, the final estimates of poverty and inequality from RUSMOD are close to those based on national accounts; hence the model can be seen as a reliable tool for evaluating the current performance of the Russian tax-benefit system and the distributive impact of potential tax-benefit reforms.

# **Input data**

In order to be
almost bound to be curtailed very quickly.<sup>7</sup> The potential employment creation following greater trade integration can be significant. Thus, in the case of Madagascar, employment in the textiles export industry grew from 47,000 to some 200,000 between 1997 and 2001, with workers earning a 40 percent premium over the average income earned in the informal sector (Nicita 2004). In fact, even giant economies benefit from large overseas markets. China’s initial take-off was fueled by agricultural reform but kept running on manufactured exports usually from EPZs and township and village enterprises (Fu and Balasubramanyan 2005). India had a fiscal boom in the late 1980s, but kept growing in the 1990s via further reforms in which trade figured strongly. To trade openness, Kee and Hoon (2005) add the benefits of openness to foreign direct investment which brings technology and forward and backward linkages. As argued by Fu and Balasubramanyan (2005), inward FDI in China played an important role in generating demand for labor.

Many studies indicate that absorptive capacity in the host country is crucial for obtaining significant spillover benefits from trade or FDI. For example, using data from industrialized countries to sixty-nine developing countries Borensztein, De and Lee (1998) tested the effect of FDI on growth in host countries and found that FDI contributes _more_ to domestic growth than domestic investment but this happens only when the host country has a minimum threshold stock of human capital. Similarly, Keller (1996) argues that access to foreign technologies alone does not increase growth rates of developing countries and he shows that if a country's absorptive capacity (measured by its stock of human capital) remains unchanged, a switch to an outward orientation may not lead to a higher growth rate. The ability of local firms to absorb new technologies is a determinant of whether better access to trade as well as the labor turnover associated with greater competition is a means of technology diffusion—in turn an important channel for growth. This suggests a
per million (ppm) by the end of the 21<sup>st</sup> century, and global mean temperature warming by between 2 and 4 ̊C (Meehl et al., 2007). For our analyses, we select “run 1” from each of the 22 climate models that archived surface temperature and precipitation data for the A1B 21<sup>st</sup> century scenario.

Given the importance of adequate precipitation for maize production in Tanzania and the world (e.g., Ahmed et al., 2011; Lobell et al., 2008), we focus our analyses on the occurrence of dry years in Tanzania and key (or potential) trading partners during the 20<sup>th</sup> and 21<sup>st</sup> century CMIP3 simulations. For the purposes of this study, we take a dry year to be any year in which the annual precipitation is equal to or less than the historical 1-in-10-year dry event. We take the historical period to be 1951-2000, meaning that for each trading partner, the 1-in-10-year dry event is the 5<sup>th</sup> driest year in that partner during the 1951-2000 period, and a dry year is any year in which the annual precipitation is less than or equal to the precipitation value of the 5<sup>th</sup> driest year of the 1951-2000 period<sup>17</sup> .

We use the CMIP3 climate model experiments to quantify the likelihood of different trading partners experiencing dry years, including the likelihood that these dry years co-occur in Tanzania and the trading partners. To do so, we first calculate the annual precipitation in each partner-country in each year from 1951 through 2000 in each of the CMIP3 historical simulations. We then calculate the magnitude of the 1-in-10-year dry event for each trading partner for the 1951-2000 period, which we may call the “dry year threshold”.

Having obtained the dry year threshold for each partner for
.

> 7 This evidence is also in line with well-established, more macro/aggregate, evidence that emerging market economies have less ...nancial depth, intermediation, and development than their developed counterparts (e.g., King and Levine, 1993; Beck et al., 2000; Beck et al., 2007). More recent micro-based evidence from individual spending data and local-based experiments also support, to di¤erent degrees of explicitness, the relevance of ...nancial fragility on the marginal propensity to consume and on the size of local STMs (e.g., Haushofer and Shapiro, 2016; Egger et al., 2019; Pennings, 2020; Gerard et al., 2020).

5
0.180|‐0.150|
||Mortgage interest subsidy|0.60|‐0.146|‐0.286|‐0.276|‐0.306|
||ElectricitySubsidy|0.13|0.325|0.185|0.195|0.166|
||HeatingSubsidy|0.42|0.034|‐0.106|‐0.097|‐0.126|
|**consumable income**|||||||
||In‐Kind Education||||||
||pre‐school|‐0.03|0.482|0.342|0.351|0.322|
||primary& secondary|‐0.08|0.531|0.391|0.400|0.371|
||tertiary|0.17|0.284|0.144|0.154|0.125|
||In‐Kind Health|‐0.05|0.504|0.364|0.374|0.344|
|**final income**|||||||

<u>Source: Authors elaboration using HSES 2016</u> <u>Note: The concentration coefficient measures the cumulative proportion of benefits (or taxes) received by each group</u> of the population ranked by level of income (or some other wellbeing indicator). A negative concentration coefficient means that poorer members of the population receive a proportion of taxes (or benefits) larger than their share of the population. The Kakwani coefficient, a measure of progressivity, is computed as the difference between income Gini coefficient of a given income definition (e.g. market income, disposable income) and the concentration coefficient of benefits (the reverse in the case of taxes). A positive Kakwani index means the tax (benefit) is progressive (regressive
Table A9: Medium-term impacts of _Mais Educação_ on educational outcomes for schools that chose digital inclusion field

|Dependent variable|Dropout rates|Test score<br>mathematics|Test score<br>Portuguese|
|---|---|---|---|
||(1)|(2)|(3)|
|Panel A: Imp|acts on fifth-grade va|riables||
|Dummy_Mais Educação_* year 2011|0.404|–1.927|–1.860|
||(0.862)|(1.967)|(1.900)|
|_R_ <sup>2</sup>|0.702|0.817|0.814|
|Observations|978|978|978|
|Dummy year 2011|Yes|Yes|Yes|
|School fixed effects|Yes|Yes|Yes|
|Panel B: Imp|acts on ninth-grade v|ariables||
|Dummy_Mais Educação_* year 2011|-0.139|-2.015|-0.414|
||(1.783)|(2.131)|(2.052)|
|_R_ <sup>2</sup>|0.694|0.804|0.807|
|Observations|916|916|916|
|Dummy year 2011|Yes|Yes|Yes|
|School fixed effects|Yes|Yes|Yes|

_Sources:_ Authors’ calculations using MEC/FNDE administrative data, _Prova Brasil,_ School Census, and INEP.

_Note:_ Treatment group are schools that joined digital inclusion field in 2008 and remained in the activity until 2011. This table reports coefficients from the ordinary least squares estimation of equation (5) in the paper, where the dependent variable is in column (1) is dropout rates, in column (2) mathematics grades in _Prova Brasil_ , and in column (3)
Policy Research Working Paper 10366

# **Abstract**

This paper examines the impact of Covid-19 on a host of labor market outcomes in India, using a continuous household survey. Cross-sectional trends of headline indicators like unemployment and labor force participation indicate a quick recovery after the unprecedented shock of the pandemic. However, women, marginalized social groups, and youth were more adversely affected, largely due to the nature of their employment before the pandemic. Next, a fixed-cohort analysis with a difference-in-differences regression framework is used to follow the labor market trajectories of a Covid-affected cohort and a pre-Covid cohort for comparison, over a span of 12-16 months. A story of downward transitions emerges, with movements out of the labor force, into more informal and lower-paid types of work in agriculture and rural areas. Higher education and being located in dense, urban areas partially mediated the adverse shocks of Covid-19. Lastly, India’s extensive vaccination campaign had significant positive impacts on employment recovery.

This paper is a product of the Poverty and Equity Global Practice. It is part of a larger effort by the World Bank to provide open access to its research and make a contribution to development policy discussions around the world. Policy Research Working Papers are also posted on the Web at http://www.worldbank.org/prwp. The authors may be contacted at heyiming23@gmail.com; anarayan@worldbank.org; polinto@worldbank.org; ssinharoy@worldbank.org; nsarma@worldbank.org.

_The Policy Research Working Paper Series disseminates the findings of work in progress to encourage the exchange of ideas about development issues. An objective of the series is to get the findings out quickly, even if the presentations are less than fully polished. The papers carry the names of the authors and should be cited accordingly. The findings, interpretations, and conclusions expressed in this paper are entirely those of the authors. They do not necessarily represent the views of the International Bank for Reconstruction and Development/World Bank and its affiliated
expression and beliefs, freedom for association and organization, and protection from the state in political, civil, and criminal matters. The Political Rights index captures free and fair elections, political pluralism, and government accountability. Each ranking is measured on a 1-7 scale. Our chosen proxy for _Political Freedoms_ is the sum of the two rankings. We have reversed the original scale to make them more intuitive, so that higher values represent more political openness. (There is some conceptual overlap in _Institutional Capacity_ and _Political Freedoms_ ; for example, both measure aspects of the rule of law. Empirically, the two indexes are correlated at .46.) As will be shown below, results obtained using the Freedom House _Political Freedoms_ indicator are robust to using alternative measures of democracy and of constraints on the executive from the Polity data base.

Institutional development as measured by our two indicators may affect the graduation decision through other channels, in addition to its implications for income volatility. In countries with greater _Institutional Capacity_ and more _Political Freedoms_ , growth is likely to be more inclusive, with the benefits of high incomes not confined to elites. Knack (2002) shows that in more institutionally developed countries (as measured by ICRG indicators), growth in incomes is most rapid for the lower-quintile groups in the income distribution in each country. Public resources are also likely to be used more effectively: revenues will be raised and expended in more efficient and equitable ways, producing higher-quality public services (see, e.g., Rajkumar and Swaroop 2008; World Bank 2003). Income inequality and human development are important outcomes in themselves; to the extent that higher-quality public services in health and education increase the human capital of workers, the likelihood that per-capita income will fall below the IBRD threshold will also decline.

As a third indicator of institutional development, we include a transition economy dummy variable for the countries of Central and Eastern Europe and the former Soviet Union. For given income levels, their economic and social institutions for sustained market-based development were
>Central Bank|Corruption<br>perception index|Governance Pillar<br>Score|Government<br>effectiveness|
|---|---|---|---|---|
|Independent Investment Committee|-0.018|-0.186*|-0.180*|-0.204**|

_Note: The significance level of the pairwise correlations is displayed with asterisks, where *** stands for a p-value below 0.01 (i.e., extremely significant), ** stands for a p-value below 0.05 (i.e., very significant), and * stands for a p-value below 0.1 (i.e., significant)._

# **Table I.8  Independence of the Investment Committee to Approve Investment Policy and Portfolio**

21
Policy Research Working Paper 10779

# **Abstract**

To adequately prepare the labor force for the green economy, policy makers and workers require a detailed understanding of the nature of green jobs. This study profiles green jobs in the South African labor market. It uses labor force survey data and applies an occupational task-based approach to identify current green occupations and associated jobs, count them, and profile their workers and wages. The findings show that 5.5 to 32 percent of South Africa’s jobs can be labeled as “green,” where the former estimate uses a strict definition and the latter uses a broad definition. The share of strictly green jobs has not changed over the past eight years. While 65 percent of strictly green occupations can be classified as high (skill) occupations, only 55 percent of workers are in these occupations, reflecting numerous employment opportunities in mid-level and elementary green occupations. Strictly green occupations tend to be male-dominated and held by prime-age (25–44) workers with post-secondary school. However, the profile of those in the greenest of the green occupations shows that they are older (age 45–65) workers and Black Africans with lower than completed high school education. Policies to prepare South Africans to engage in the green economy include developing a strategy to teach new and existing workers to use green technologies; targeting green occupations in youth development programs; making a concerted effort to support women in science, technology, engineering, and mathematics; helping low-skilled green workers to organize and improve their work conditions; and continuing to collect and analyze data for better tracking South Africa’s progress in becoming a green labor force.

This paper is a product of the Social Protection and Jobs Global Practice. It is part of a larger effort by the World Bank to provide open access to its research and make a contribution to development policy discussions around the world. Policy Research Working Papers are also posted on the Web at http://www.worldbank.org/prwp. The authors
activities. They report a variety of elasticities in their paper.7 We adopt their estimate of 2.67 for use in this work as the overall farm/off-farm transformation elasticity for the total rural labor force.

To obtain separate estimates of the farm/off-farm transformation elasticity for three skill levels of labor, we utilized the rate at which increased schooling attainment enhances the transformation elasticity,<sup>ε</sup> _l_<sup>, based on the study by Zhang, Huang and Rozelle (2002). These</sup> authors explore the labor-supply behavior of a panel of 310 individuals in 109 families observed in four villages of Jiangsu province in the years 1988, 1992, and 1996. They find that for every additional year of education, farmers had a 14% greater chance of finding an off-farm job in 1996, _ceteris paribus_ . Using the base year ratio of the shadow value of labor in agriculture relative to the off-farm wage rate, as well as the total farm and off-farm labor supplies, this increasing opportunity to access off-farm jobs associated with higher educational attainment translates into an increment of 0.58 in transformation elasticity for each additional year of schooling. This is used to calculate the farm/off-farm transformation elasticities by skill level, according to the average years of schooling for each skill level of the rural labor force. The resulting values for this elasticity are 0.68 for unskilled labor, 4.01 for semi-skilled labor and 7.49 for skilled labor.

> 7 Due to the variety of labor supply elasticities in response to the three different wages in their model, the authors obtain a variety of labor transfer elasticities, depending on the “thought experiment” being conducted. These are asymmetric, with the response to a change in shadow wages differing from the response of labor supply to a change in the market wage. However, this response is treated as symmetric in our model. This makes it difficult to choose the correct parameter for our analysis. We focus on the transfer