rafmacalaba/data-use-mentions-extended huggingface dataset:
towards goods that have become relatively cheaper over time (Deaton 1998). This _commodity substitution bias_ may be particularly relevant in Sub-Saharan Africa, where many countries lack regular HBS programs. According to metadata of the International Labour Organization (ILO 2013), which depict national practices in computing CPIs as of July 2012, 13 percent of the population in the region was living in countries in which the baskets were based on data from the 1990s, and an additional 23 percent was living in countries were basket weights dated between 2000 and 2004 (see Beegle et al 2016).

A related issue is that CPIs often do not reflect differences in the prices of the same product purchased at various outlets if distribution channels change over time. This is referred to as the _outlet substitution bias_ . In many African countries, for example, the popularity of supermarkets that offer discounted prices has been growing, at least in urban areas. Because the CPI does not account for the shift among consumers towards these more economic retail outlets (for items previously purchased in traditional stores), it overstates actual inflation.

CPIs also do not account for changes in the quality of existing goods and services in the basket, which leads to a _quality change bias_ . Over the years, as technology has evolved, many products have exhibited dramatic improvements (for example, greater functionality or safety, greater nutritional value, and so on). However, it is typically very difficult to separate out how much of the price change recorded for a specific product is associated with a change in quality and how much is associated with actual inflation.<sup>3</sup> Similarly, it often takes many years until newly available goods and services are introduced into the CPI computation, the _new product bias_ . Because the CPI fails to capture the benefits to consumers from greater availability of products and brands, changes in the cost of living are typically overstated.

The CPIs in many countries in Sub-Saharan Africa suffer from additional sources of bias and measurement error. An important example
, we calculate the “descriptive elasticity”: The annualized change in poverty during a spell divided by the annualized change in survey mean consumption/income per capita during the spell (alternatively: GDP per capita). This results in an estimate of the GEP: the annualized change in the poverty rate for each percent change in per capita mean or GDP.

For the purposes of this paper, we are mainly interested in the elasticity of poverty to economic growth, as measured by growth in GDP per capita. It is this growth rate of GDP that is routinely reported by countries and international organizations, and which is often a target in economic development plans and policies. And it is also by this yardstick that the economic performance of countries is evaluated. We thus assess to what extent headline economic growth translates into improved household welfare, though we also report the elasticity with respect to survey means.<sup>8</sup> As one might argue that the concept of GDP includes many components that do not directly affect the lives of the poor, we also look at the elasticity between household final consumption expenditure as measured by national accounts (henceforth HFCE) and poverty.

Annex Table 1 presents the descriptive elasticities based on the full dataset of 1,604 spells. The summary statistics suggest that some data cleaning will be required to handle extreme values: The descriptive elasticities of poverty with respect to survey means range from -26,348 (indicating that a 1 percent increase in survey means is associated with a 26,348 percent reduction in poverty) to 2,593 (a 1 percent increase in survey means is associated with a 2,593 percent increase in poverty), while the elasticities with respect to GDP per capita range between -8,114 and 9,393. The outliers have a large influence on the average elasticity in our dataset of consecutive spells: The average GEP across all spells amounts to an implausibly large (in absolute value) -17.6 when growth is measured by survey
|Capital (log)|13.7|2.1|
|Capital per worker|9.3|1.4|
|Labor productivity|10.3|0.9|
|Proft rate|0.3|0.2|
|**_Firm shock_**|||
|Firm shock in 2008 (negative of growth in GDP of destinations)|-1.0|2.2|
|Total number of frm-year observations||24,094|
|Total number of unique frm IDs||2,859|

_Note:_ The worker-level statistics are based on the sample of workers in firms with a non-missing GFC firm shock measure in 2008 used in the regressions. Real wages are expressed in USD in 2010 prices. The firm-level statistics are based on all firms with non-missing GFC shock measure in 2008 used in the main regressions. All statistics for which a time period is not shown in parentheses cover the 2009-2017 period.

58
objects; and helping child with any homework. Typical behavior control strategies or disciplinary techniques were also measured, adapted from the MICS.<sup>33</sup> Two scores were derived from this module: ( _i_ ) the _Stimulation Index_ sums over a set of indicators describing activities that the primary caregiver does with the child (13 variables at baseline and the 18-month follow-up, and 18 variables at the 36-month follow-up); ( _ii_ ) the _Positive Practices Index_ sums over a set of six indicators for the use of positive methods to address child behavioral problems. Both the _Stimulation Index_ and the _Positive Practices Index_ were standardized at each round (baseline, 18-, and 36-month follow-up) using the control group’s mean and standard deviation.

We used Inverse Covariance Weighting (ICW) as described in Casey et al. (2012) to generate a _Parenting Quality Index_ by aggregating over parenting-related variables ( _Stimulation Index_ , _Positive Practices Index,_ and _Parental Stress Index_ ) at baseline and the 18-month follow-up. The ICW methodology is described in detail under _Kaufman Assessment Battery-Children, 2_<sup>_nd_</sup> _Edition (KABC-II)_ above. The _Parental Quality Index_ is not computed at 36-Month Follow-Up since we did not administer the _Parental Stress Index_ during that round.

# **Community-based child care center (CBCC) quality measures**

We used two strategies for gathering data on the quality of the CBCCs to estimate impacts of the interventions on the functioning of the centers. These included a questionnaire, completed by the director and teachers; and an observational tool, completed by a pair of enumerators during typical operation of the center. The CBCC questionnaire and observation measures were adapted from the _La Escala de Evaluación de la Calidad Educativa de Centros de Educación Preescolares_ (ECCP) (Martínez et al., 2004) from Mexico, and a preschool quality tool used in Cambodia (Rao et al., 2012). Items on both tools were based on theoretical and empirical data recommending assessment of both the
economic tool can create jobs, which will enhance women’s chances of having better livelihoods and help bridging the gender inequality.

# 1.2 Study Objectives

The study was inspired by the increasing evidence of growing gender wage gaps and declining female labor force participation and the little evidence on gender impacts of freer trade in general or green trade in particular. Comparisons of gendered economic impacts of free trade across these countries are impeded by the lack of a gender differentiated data set. Most of these quantitative analyses of trade reforms use global computable general equilibrium (CGE) models which illustrate the linkages between sectors within an economy and the linkages between countries. These global CGE models have become the standard quantitative tool in assessing the impacts of trade reforms on sectoral adjustments, patterns of production and trade, etc. and are mostly based on the Global Trade Analysis Project (GTAP) database, which is a multi-regional, multi-sectoral database designed to capture the economy-wide relationships among sectors in a region and interregional trade. These models have also been used to assess the impacts of other recent global shocks such as the food crisis and financial crisis, bioenergy mandates, demographic changes including migration, and climate change. However, since the present GTAP database does not contain a gendered employment database across countries and sectors and also does not isolate the commodities relevant for green trade. there have been no attempts at assessing the impacts of multilateral reforms using a gendered global model. To address this gap in the literature, this study aims to construct a gender differentiated database across countries and sectors.

The objectives of the study are therefore:

- To develop a gender differentiated employment and wages database in the current GTAP database version 9 for select South Asian economies across sectors and modify the standard GTAP model to recognize gender differentiated employment and wages.

- To identify goods and services related to green trade and split them as separate commodities in the newly constructed GTAP database and model.

- Using the newly constructed model and database, the study identifies targeted value chains and economic activities,
_Business_ report, is assessed by the Freedom House’s Freedom of the Press rating as not free (Pakistan, Russian Federation, Malaysia, United Arab Emirates)<sup>8</sup> ; in five as only partially free (India, Mexico, Philippines, Italy, Nigeria) and in only one – Spain – as completely free. As further discussed in the analysis section, to eliminate the bias of authoritarian governments’ control over media, we perform panel data analysis excluding authoritarian and semiauthoritarian governments.

**Figure 4:** _Doing Business_ media coverage by income group and reforms implemented in 2004-17

|**Income group**|**Media articles**<br>(number, Factiva 2004‐17)|**Number of**<br>**implemented reforms**<br>(2004‐18)|
|---|---|---|
|Lower middle income|6,530|972|
|High income|5,684|839|
|Upper middle income|4,759|882|
|Low income|503|495|

# **Variable selection and summary statistics**

One of the key outcome variables used to test the set hypothesis is “number of _Doing Business_ reforms” in a given country in a given year. The number of implemented _Doing Business_ reforms is a simple count of reforms carried out by each country per _Doing Business_ cycle. The sum of this variable can fluctuate from 0 (no reforms) to 10 reforms,<sup>9</sup> which is the maximum possible number of reforms, assuming a country reformed on each of the _Doing Business_ indicators.

Aside from the reform count, _Doing Business_ presents data in terms of two main aggregate measures: the _Doing Business_ score and the ease of doing business ranking, which is based on the Doing Business score. The ease of doing business ranking compares economies with one another; the Doing Business score
to explore lower-cost alternatives to achieve the objectives pursued by such measures and study of ways in which the contracting parties adjust to minimize observed efficiency losses would be desirable.

The paper is organized as follows. Section 2 sets the stage by reviewing land reform in India, highlighting the conceptual basis and empirical estimates of the ‘Marshallian inefficiency’, and laying out key hypotheses and estimation strategy. Section three provides descriptive evidence from a comprehensive listing of some 90,000 households and a detailed follow-up survey of about 2,000 owner-cum tenants in West Bengal. Section 4 discusses econometric results on productivity- and investment- effect of share tenancy in the cross section and, using household fixed effects, the owner-cum tenant sample. Section 5 concludes by drawing out implications for policy in West Bengal and beyond.

# **2. Motivation and analytical strategy**

Land reform can be justified socially if it increases incentive for investment in non-contractible inputs. Indeed, land reform in West Bengal, one of the few Indian states that decisively pursued implementation of this policy in the late 1970s, is widely credited with having caused a spurt of agricultural growth in the 2
cannot be simulated without strong assumptions.

# III. Performance of the Russian tax-benefit system

## **Baseline scenario: Actual incidence of the Russian tax-benefit system as of 2017**

**The fiscal system in Russia reduces poverty by half and inequality by a third.** The poverty rate under the original income would be 34 percent and the Gini index 50 — this is reduced to 15 percent and 35 respectively (Table 1). The bulk of redistribution happens between the _original_ and _disposable income_ —that is, at the point of direct taxes/benefits, and before indirect taxes/subsidies. The inequality of _consumable income_ (that is, after indirect taxes/benefits), increases somewhat (by 0.7 percentage points), reflecting the slight regressivity of the VAT.

**Table 1. The overall redistribution effect of the tax-benefit system in Russia**

||Original<br>income|Disposable Income|Consumable<br>Income|
|---|---|---|---|
|**Poverty rate, percent**|34.0|14.7|21.1|
|<br>absolute change w.r.t original income, p.p.||-19.2|-12.9|
|relative change w.r.t. original income, percent||-56.7|-38.0|
|**Gini index**|49.6|34.8|35.5|

> 5 For example, a _change_ in tax revenues as derived from the model can be applied to the actual budgetary data on revenues to obtain impact in absolute terms (such as trillions of rubles, or percent of GDP).

6 In fact, in-kind programs typically play an important role in reducing inequality. In theory, these could be simulated using auxiliary models and/or strong assumptions. This is however not attempted here for reasons of tractability
Policy Research Working Paper 8763

# **Abstract**

Using panel data for 189 economies from 2005 to 2013, this paper shows that business-friendly regulations are correlated with the poverty headcount at the country level. This association is significant using the World Bank’s Doing Business indicators on getting credit and contract enforcement. The findings suggest that the conduit for poverty reduction is business creation, as a source of new jobs and a manifestation of thriving entrepreneurship.

This paper is a product of the Global Indicators Group, Development Economics. 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/research. The authors may be contacted at sdjankov@worldbank.org,dgeorgieva@worldbank.org, and rramalho@ifc.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 organizations, or those of the Executive Directors of the World Bank or the governments they represent._ Produced by the Research Support Team
131 Information presented here. The CPS and DHS data are available, accessible and of reasonably high quality. They deserve to be further analyzed along the above dimensions.

Thus, both an effort to exploit existing information and to collect broader and more focused information on consumer behavior in family planning decisions are warranted. Moreover, a more rigorous approach is called for given the emphasis to date on anecdotes and the dearth of data. Both data and analysis of existing (and nev) data could contribute importantly to a better understanding of the role and contribution of the private for-profit and NGO sectors.

Costs and Cost Effectiveness. As part of the reviev of what is known concerning the private for-profit sector and how it compares vith other providers, the cost and cost-effectiveness of alternative providers vas explored. Little good evidence exists on the costs of government programs, commercial providers consider costs proprietary information, and social marketing programs have never been costed out. The NGOs have probably the most available cost data, although it is virtually always incomplete and only available for some endeavors. The majority of this evidence is compiled in Levis (1985).

The lack of cost information prevents comparisons of the cost effectiveness of alternative financing and delivery arrangements. No guidance is available on the most efficient means of delivering family planning to different income, location, education, and age groups. Even the industry-based programs have by and large ignored the (full) cost of their programs. For example, there are no plans to compare costs and effectiveness across projects to determine the best approaches to fostering demand for family planning or promoting activities by the private sector. Cross-project evaluations are neither mandated by any project nor anticipated by any entity. Assessing the costs, effectiveness, and

**L.fp:Faaily** Plaming **Paper (whole)**
# **4 Social Capital, Socio-Demographics and Trust**

While this paper focuses on institutional trust, the survey also contained variables related to social trust. In the literature, the concept of social trust is often closely linked to the concept of social capital popularized by Putnam. Social trust represents trust between individuals within society, across a range of social ties. Social trust can be measured by asking respondents to assess their degree of trust to a generalized ‘other” (radius of trust), or to a list of di↵erent categories of individuals, starting from those who are close (e.g. friends and family) and extending to those who are more distant (e.g. people met for the first time, foreigners).<sup>19</sup> In this section, we explore the interaction between social trust and institutional trust.

We estimate the relation between institutional trust and social trust, as well as other socio-demographics, using the following model: Controls include a range of socio-demographic variables including: gender, urban/rural location, age, education, and income.

Levels of social trust in Morocco appear to be quite low. The survey data indicates that only 56 percent of respondents think that Moroccans trust each other. It also shows that interpersonal trust in Morocco is disproportionately geared toward in-groups, such as family, friend, and neighbors, rather than toward more distant groups such as individuals from a di↵erent religion, locality or nationality. Specifically, 94 percent report having high or very high levels of trust in their family; compared to 63 percent who report high or very high trust levels for friends, 63 percent for neighbors, 60 percent for work colleagues, and as low as 28 percent for people of other locality or nationality.<sup>20</sup> This echoes a previous World Bank study that found that “while low trust in outgroups is a

> 19Generalized social trust is measured in standard survey items by simply asking whether respondents feel
# Figure 3: Discriminatory Practices and GDP

<!-- Start of picture text --> Discriminatory Practices Index Discriminatory Practices Index<br>Discriminatory Practices Index Regression Coefficient: -2.55<br>Median=0.265 Standard Error: 0.77<br>.1 .2 .3 .4 .5 .6 0 .2 .4 .6 .8<br>Index of Discrimination in Customs Index of Discrimination in Customs<br>(0=No Discriminatory Customs, 1=Extremely Discriminatory Customs) (0=No Discriminatory Customs, 1=Extremely Discriminatory Customs)<br>.25 12<br>.2<br>10<br>.15<br>8<br>.1<br>Fraction of Countries<br>6<br>.05 Log of 2009 Per-Capita GDP<br>0 4<br><!-- End of picture text --> Index of Discrimination in Customs is an average of the values of the following subindices from the 2009 OECD's Social Institutions and Gender Inequality (SIGI) Index: Early Marriage (Women); Acceptance/Legality of Polygamy; Freedom of Movement; Obligation to Wear a Veil; Violence against Women; Female Genital Mutilation
OLS estimates, as per equation (4). Dependent variable is ln(earnings). Sample includes employed individuals aged 15-65. Income bracket weights applied. The ‘baseline’ columns control for survey quarter dummies and 9 province dummies. The ‘Full’ columns add demographic characteristics (gender, age, education, and race), 9 main industry dummies, a location dummy and 9 main occupation dummies to the variables in the baseline model. Source: PALMS v3.3 data. Our variable of interest is the variable _green_strict_ which is a binary variable equal to one if an individual is in a green job.

# 5 Conclusion and Policy Directions

This paper aims to profile green jobs in the South African labor market. We utilize the PALMS labor force survey data 2012-2019 and apply an occupational task-based approach to identify green jobs, count them, and profile the workers and the wages for green jobs. Through the process, we adapt a green terms dictionary to be relevant for the South African labor market.

We find that 5.5 percent of South Africa’s jobs can be classified as strictly green, while 32 percent can be classified as broadly green. The share of strictly green jobs is in the range of estimates from other countries, spanning 2.3 percent in Indonesia to 39 percent in the UK. South Africa’s share of strictly green jobs has barely changed over the last 10 years, while broadly green jobs are slowly increasing. This may be a statistical anomaly driven by the limitations in our data that do not allow us to update the task content of jobs as they evolve. Alternatively, it may be a signal that strictly green job growth is stagnant in the South African economy, even as broadly defined green sectors are growing. This brings into question if today’s green policies are sufficiently transformational to shift the South African economy, and its labor force, to greener jobs.

While 65 percent of South Africa’s strictly green occupations can be classified as high-level, only 55
0.024% increase, or doubling, of the emigration flow.

> 10 Many countries do not break down country of origin beyond the top ten or so sending countries.

> 11 Migration flow data collected from the Office of Immigration Statistics, Department of Homeland Security in the United States; Citizenship and Immigration Canada in Canada; the Department of Immigration and Multicultural and Indigenous Affairs in Australia; and the New Zealand Immigration Service in New Zealand.

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