rafmacalaba/data-use-mentions-extended huggingface dataset:
as a VAT payer (SARS,2003). Some services provided within the financial sector are also exempt. (International VAT Monitor,1995:376).

In this paper, we take the economic and tax structure of the SAM, including the VAT and its exceptions, as the basis for the CGE model and we examine possible tax reforms that will improve welfare impact.

# **2.2 Specification of a VAT with Rebates on Intermediate Inputs**

Rather than treat consumption tax payments as a retail sales tax, we specify a European-style value added tax with rebates for intermediate input and investment purchases. With this treatment, there is no cascading effect on prices of taxes on intermediate goods.

We assume the VAT is administered using the “invoice method.” All transactions are taxed at a fixed proportional rate regardless of whether they are final or intermediate transactions. Firms can deduct taxes paid on intermediate inputs and that tax amount is reported on the invoices for intermediates. Import sales are subject to a VAT, export sales are not.

To calibrate the model, we assume the observed VAT tax revenue in the SAM is the gross payment by sector.<sup>8</sup> Producers must pay the VAT on retail sales and can collect a rebate on taxes paid by intermediate input use. For each production activity, we compute the rebate on intermediate expenditure using data on input-output relationships. We assume a Leontief production structure with fixed input-output shares defining intermediate demand.

The rebate to activity, A, is based on total intermediate input use: _REBATE_ ( _A_ ) = ∑ _tvat_ ( _C_ ) ⋅ _PQ_ ( _C_ ) ⋅ _QINT_ ( _C_ , _A_ ) _c_ where: tvat(C) is the value added tax by commodity sales PQ(C) is price of commodity c QINT(C, A) is the quantity of intermediate commodity, c, purchased by activity, a The price of the aggregate intermediate input now includes the rebate per unit of aggregate intermediate input: _PINTA_ ( _A_
# **Digital divide**

The UK has relatively high Internet penetration, but the ONS nonetheless reported 15% of adults did not use the Internet in 2014 (when most of these studies were conducted). Additionally, there are still substantial gaps in digital literacy even among those with access to the Internet (Office for National Statistics, 2019).

As Figure 4 shows, digital literacy varies across demographic groups. The largest demographic splits are by age and education. Older British people are much more likely to be digitally illiterate in the UK and lack Internet access (Office for National Statistics, 2019). In 2017, 13% of those aged over 50 lacked Internet access, compared with 2% of those aged 50 or under (Internet Access, Opinions and Lifestyle Survey (OPN), 2017). The education gap is even stronger: 26% of people with no qualifications lack Internet access compared with 1% of those with a degree (Office for National Statistics, 2017).

Other demographic groups show less dramatic splits. Women have slightly lower digital literacy than men on average (Office for National Statistics, 2019), but broadly similar levels of Internet access (Office for National Statistics, 2017). Ethnic minorities are _more_ likely to have Internet access in the UK, although this difference disappears after accounting for age.

# **Digital divide → participant profile**

We analyze FMS users’ demographics by comparing a survey conducted by Cantijoch, Galandini and Gibson (2016) to the UK Annual Population Survey to look at how over or under-represented different groups are. Men are substantially overrepresented on FMS (66% of users) and the average age of FMS users is much higher than the whole adult population (55 years compared to 48 in the adult population). Ethnic minorities are substantially underrepresented on FMS (2% compared to around 12% of UK adults in the 2016 Annual Population Survey). However, the single largest overrepresentation on the platform is of highly educated people: 80
the backcasted series using a pass-through of 1 and using sectoral GDP data mapped to household head’s employment sector to backcast the 2018/19 consumption vector. Column 3 shows the results of interpolating between the 2003/04 NLSS and 2018/19 NLSS without using the HNLSS 2009/10 estimate and following PovcalNet’s methodology (Prydz et al. 2019). Column 4 and 5 show the results of survey-to-survey imputations using GHS data from different waves and 2018/19 NLSS as a baseline survey. Column 4 shows the imputed estimates when using a full set of non-monetary indicators, column 5 shows the imputed estimates when excluding from the consumption model potentially seasonal variables such as food-consumption dummies and employment indicators.

Table 16 Main estimates using different sources of population data, WDI and NBS

||Number of<br>line (million<br>estimates<br>|poor at the US$1.9<br>s) – using NBS pop<br>|0 poverty<br>ulation<br>|Number of poor at<br>using WDI populati<br>|the US$1.90 poverty li<br>on estimates<br>|ne (millions) –<br>|
|---|---|---|---|---|---|---|
||NLSS|Backcasts<br>(pass-through<br>1)|S2S<br>Imputation|NLSS / PovcalNet|Backcasts (pass-<br>through 1)|S2S<br>Imputation|
|2009/10|91.1|||89.4|||
|2009||73.8|||71.4||
|2010||70.0|||67.8||
|2011||70.2|||68.1||
|2010/11|||72.0|||
a set of exogenous variables i.e. income, education, age and gender. Accordingly, using formula 2, we estimate Maximum Likelihood as a series of probit models. The marginal effects on the latent variable are calculated from the models, where the coefficients represent the change in the probability of using formal financial services when, for example, β changes, all other things being equal.

This paper builds on the above model, with two modifications as follows:

- In place of income levels of individuals, our analysis uses the variable “type of employment” as a proxy, given that the NFI survey (2018), does not capture the income levels of individuals.

- “Use of digital cards” was included as a fourth indicator of formal financial inclusion ( _finInc)_ . This is similar to the work by Tuesta et al. (2015) where digital financial inclusion was considered. Our indicator captures use of debit and credit cards at either an ATM or a retail store.

Using Formula 2, the first set of results aim to understand the determinants of financial inclusion based on socioeconomic factors. This is followed by the second set of results which probe further into determinants of informal finance. Tables A.1 and A.2 in the Appendix describe the variables included in each of the regressions in detail.

# **4. Empirical Results**

# **4.1. Factors That Drive Formal Financial Inclusion**

As stated in the previous section, we consider the following four products/services as indicators of formal financial inclusion: (1) having an account in a formal financial institution ( _account_ ), (2) having a loan from any institution in the formal financial system ( _formal credit_ ), (3) saving at an institution in the formal financial system ( _formal savings_ ), and (4) usage of credit or debit cards at an ATM or a merchant outlet ( _digital cards_ ).

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several papers on Latin American countries, including Brazil (Huneeus et al. 2015), Chile (Huneeus, Landerretche, and Puentes 2012; Sehnbruch 2006), Colombia (Gómez-Salcedo, Galvis-Aponte, and Royuela 2016), Ecuador (Villacís and Reis 2016), and Mexico (Ortega Díaz 2013). The job quality studies on poor countries are scarce (For example, on Benin, see Kponou and Kamga 2019).

Given the paucity of indicators and the difficulty in harmonizing micro level data across countries, the existing cross-country literature is thin. Instead, studies predominantly focus on developed countries with more granular data or on a single region with comparable surveys. There have been several attempts to benchmark job quality across European countries, commonly using the European Working Conditions Survey (EWCS) (Eurofound 2012; Muñoz de Bustillo et al. 2012; Simões, Crespo, and Pinto 2015; Smith et al. 2008). The main advantage of the EWCS is that it provides comparable micro level data for European countries that encompass a wide range of information on job quality. In addition to more common job characteristics, such as earnings and job security, the EWCS also includes information on less common features, such as career progression, level of autonomy, and pace of work (Eurofound 2012). Nonetheless, a major disadvantage of this dataset is the limited sample size and the inability of researchers to use the data to explore heterogeneity in job quality across sociodemographic characteristics, such as age, educational attainment, sector, or region (Leschke and Watt 2014). To circumvent this issue, Erhel et al. (2012) use the European Union Statistics on Income and Living Conditions dataset to explore withincountry inequalities in three dimensions (nonstandard employment, working time and skills, and career development) in 24 European countries.<sup>7</sup> Brisbois (2003) uses the EWCS dataset, together with the Ekos

> 5 See Measuring Decent Work (dashboard), International Labour Organization, Geneva, https:/
Policy Research Working Paper 6271

# **Abstract**

This paper investigates the urbanization of the Indian manufacturing sector by combining enterprise data from formal and informal sectors. It finds that plants in the formal sector are moving away from urban and into rural locations, while the informal sector is moving from rural to urban locations. Although the secular trend for India’s manufacturing urbanization has slowed down, the localized importance of education and infrastructure has not. The results suggest that districts with better education and infrastructure have experienced a faster pace of urbanization, although higher urban-rural cost ratios cause movement out of urban areas. This process is associated with improvements in the spatial allocation of plants across urban and rural locations. Spatial location of plants has implications for policy on investments in education, infrastructure, and the livability of cities. The high share of urbanization occurring in the informal sector suggests that urbanization policies that contain inclusionary approaches may be more successful in promoting local development and managing its strains than those focused only on the formal sector. Cities are evolving in India from places of goods production to forges of human capital and coping mechanisms for survival.

This paper is a product of the Economic Policy and Debt Unit, Poverty Reduction and Economic Management Network. 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://econ.worldbank. org. The author may be contacted at EGhani@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
644|4,644|4,644|
|N2 (target survey)|698|698|698|698|698|698|698|698|
|**True poverty rate in TA 1**<br>||||21.0<br>(2.2|<br>)||||

**Note** : The standard errors in parentheses are adjusted for complex survey design. All estimates are obtained with the population weights. Method 1 uses the normal linear regression model with the theoretical distribution of the error terms and Method 2 uses the empirical distribution of the error terms. Both methods employ cluster random effects. Models 3.1 and 4.1 employ non-standardized variables. Models 3.2 and 4.2 employ variables in TA 3 standardized by those in NPS 2020/21. Food and non-food expenditures, household size, and age in both surveys are transformed to normality using the Box-Cox method before standardization. Imputed poverty rates for TA 3 use the estimated parameters based on the 2019/20 data. 1000 simulations are implemented. The true poverty rate for TA 1 is the estimate directly obtained using the consumption data in TA1 and the poverty line in NPS 2020/21 deflated to 2022 prices using the annual WB deflator. The estimates shown in bold or with an “a” respectively fall within the 95% CI or one standard error of the true poverty rate. The underlying regression results are provided in Appendix A, Table A.6.

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Yang, C.-H. and Chen, Y.H. (2012) R&D, productivity, and exports: Plant-level evidence from Indonesia. Economic Modelling 29: 208-216.

**Figure 1: Informal competition and R&D activity**

<!-- Start of picture text --> 25%<br>20%<br>20%<br>15% 14%<br>10%<br>5%<br>0%<br>Below median informal competition Above median informal competition<br>Percentage of firms that spent on R&D<br><!-- End of picture text --> Source: Based on author’s own calculations using Enterprise Surveys (ES) data. Note: For the median value of informal competition variable used to define the two groups plotted on the horizontal axis, we first compute the proportion of all firms in the country other than the firm in question who report competing against informal firms ( _Informal Competition_ variable as defined in section 2). Median value of the variable is used to define the two groups.

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<mark>e0118571.</mark> **<mark>[</mark> 1]** <mark>Neumann, K., Sietz, D., Hilderink, H., Janssen, P., Kok, M., & van Dijk, H. (2015b). Environmental drivers of human migration in drylands–A spatial picture.</mark> _<mark>Applied Geography</mark>_ <mark>,</mark> _<mark>56</mark>_ <mark>, 116-126.</mark> **<mark>[</mark> 1]** <mark>Niva, V., Kallio, M., Muttarak, R., Taka, M., Varis, O., & Kummu, M. (2021). Global migration is driven by the complex interplay between environmental and social factors.</mark> _<mark>Environmental Research Letters</mark>_ <mark>,</mark> _<mark>16</mark>_ <mark>(11), 114019.</mark> **<mark>[</mark> 1]** <mark>Nordhaus, W. D., & Moffat, A. (2017).</mark> _<mark>A survey of global impacts of climate change: replication, survey methods, and a statistical analysis</mark>_ <mark>(No. w23646). National Bureau of Economic Research.</mark> **<mark>[</mark> 3]** <mark>Ocello, C., Petrucci, A., Testa, M. R., & Vignoli, D. (2015). Environmental aspects of internal migration in Tanzania.</mark> _<mark>Population and Environment</mark>_ <mark>,</
factors, upstream innovations have permitted a representative Mexican fertilizer company to improve its operating margin by 9.7 percent.<sup>5</sup> Based on the Mexican case study, it was realized that the only way to get a meaningful sense of what is happening in such economies in terms of upstream-downstream linkages is to design a purpose-specific set of questionnaires. Available enterprise-level panel datasets were not sufficiently detailed to allow econometric analysis of these linkage issues. In particular, panel datasets generally do not include disaggregated data on the number of providers of specific logistics services employed by downstream users and conversely on the number of customers served by providers of innovative services. They do not allow inferences to be made about the relative importance of upstream innovations versus other locality-specific institutional factors on the level of costs and sales, and on the introduction of new goods and services by downstream firms. More generally, manufacturing censuses typically do not ask questions about changes in upstream business service availability and quality. As well, censuses generally are less comprehensive in their coverage of new entrants.

**Hypotheses.** The analysis begins downstream, by exploring the impact of innovative upstream services on user enterprises.  It then explores what factors drive or hinder the development by the upstream service providers of these innovations.

At the downstream user enterprise level, the hypotheses to be tested are: _H1: Improved performance in user enterprises is significantly positively associated with upstream innovations in business service delivery._ The focus is to explore the linkages between utilization of innovative road freight logistics services by user enterprises, on the one hand, and various measures of improved performance by user enterprises (especially cost reductions and revenue increases), on the other hand.

_H2: The benefits of upstream business service innovations as they impact on user enterprises are skewed towards larger and older, established enterprises within the intensive-use sectors._ The focus is to better understand the relative importance of variables such as enterprise size, age (start-ups versus more established firms), foreign ownership, and the
in La Paz (see RHS map).<sup>18</sup> Figure (9) shows output and price differences for potatoes. The lowest price is found in Cochabamba, where output per capita is the highest (among the three regions considered in this analysis). The converse happens in Santa Cruz where output per capita is the lowest, and the price is the highest.

> 18 The 13.4%-higher price in Cochabamba relative to La Paz is explained by the packaging being different. The price information collected by the INE for Cochabamba is that of sugar bags of one-pound weight, whereas for La Paz and Santa Cruz, they collect information on sugar bags of 5 kilos.

15
.8 percent per year between 1994 and 2005, and increased four‐fold to 3.3 percent per year between 2005 and 2012, averaging more than 5 percent annually during the last two years of this period (Figure 3).

Consumption growth for the bottom 40% of the distribution has however lagged slightly behind the average growth for the population through the entire period (Figure 3); which implies a declining share of the bottom 40% in total consumption expenditure, even as overall consumption inequality has remained almost unchanged since 2005.<sup>4</sup> Thus there is room for improvement in the extent to which growth is “inclusive” or benefits the least well‐off in society – poverty reduction in India, even during its best years, would have been even higher if consumption growth in the lower half of the distribution had matched or exceeded the average gains for the population. Interestingly, the decline in the share of the two bottom quintiles in total consumption has occurred even with an almost unchanged consumption

> 3 See World Bank (2015), Basu (2013) and Narayan et al (2013) for the normative properties, interpretation and empirical measurement of this indicator. The welfare aggregate – household income or consumption – is typically selected to be the one that is used for poverty measurement in the country.  For India, shared prosperity is estimated by the growth in consumption of the poorest 40 percent in the NSS consumption surveys.

> 4 This follows readily from the following algebraic property of the shared prosperity indicator: the growth of per capita consumption of the bottom 40% is decomposable into (i) the change in the _share_ of total consumption accruing to the bottom 40% and (ii) the growth of the average consumption of the total population (Rosenblatt & McGavock, 2013).

4
the predictions associated with each tree by a factor _η_ , which is often called the learning rate. The predicted value of the outcome at iteration _t_ is then given by _yi,t_<sup>_p_=</sup><sup>_y_</sup> _i,t_<sup>_p_</sup> _−_ 1<sup>+</sup><sup>_ηft_(</sup><sup>_xi_).Shrinkage reduces the influence</sup> of each tree so that no individual tree has too much influence on the ensemble prediction.

One possible approach to hyperparameter selection is to use a grid search where the user (1) specifies the domain of the hyperparameters, (2) applies the model to every combination of hyperparameters, and (3) selects the hyperparameters that perform the best in terms of some metric (e.g., mean squared prediction error in the validation dataset). Grid search, however, is computationally inefficient because hyperparameter proposals are uninformed by previous evaluations. We instead use a Bayesian hyperparameter optimization procedure, which sequentially updates a probability model that relates the hyperparameter space to the performance metric and then uses the model to intelligently explore the hyperparameter space (Bergstra et al., 2011). Specifically, our implementation of gradient boosting relies on the open-source library XGBoost, which we combine with the Hyperopt library that conducts Bayesian hyperparameter optimization using the tree-structured Parzen estimator.

Finally, regarding our application to the Mexican data, recall that mapping procedures entail

> 12This is known as the “exact greedy algorithm.” XGBoost also has an approximate algorithm that can be used for large datasets. See Chen and Guestrin (2016) for details.

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, and any subsidiaries, if applicable.<sup>3</sup> The dataset also includes firm name, industry affiliation, headquarters country, and entity type, which we use in our analysis. Annual sales data, converted to constant 2020 U.S. dollars, are available but not used in our study.

We combine the two aforementioned FactSet data modules to construct a final dataset of 169,420 entities with bilateral relationship information from 2012 to 2022. These entities are defined as de jure corporate institutions (e.g., firms and affiliates), with 27% (46,000 entities) having available information from FactSet Fundamentals for at least one year within the sample period.

To measure carbon emissions at the firm level, we draw on the ISS ESG Carbon and Climate Impact (ICC) data from FactSet. This data set provides comprehensive coverage of emissions

> 1In financial reporting, a 10-Q is a company’s quarterly report with unaudited financial statements, while the 10-K is an annual report with audited financials. An 8-K updates shareholders on significant unscheduled events. U.S. listed firms must detail any customer accounting for more than 10% of revenue (Gofman and Wu, 2022), with some firms voluntarily disclosing additional customers (Huang, Lin, Liu, and Tang, 2023).

> 2Many Latin American records from 2014 suggest backward updating for these firms.

> 3The parent or ultimate parent is identical to the company itself for self-owned firms.

5
cigarettes is significantly lower (19 percent). Meanwhile, bidi consumption clearly declines as household resources increase. While a quarter of the poorest households consume bidis, this is only true of 6 percent of the richest households. For smokeless products, consumption is similar, except among the top deciles. This is consistent with other studies describing the socioeconomic gradient in tobacco use; the gradient is not seen in cigarette consumption, but is noticeable in the consumption of bidis and smokeless products (Barkat et al. 2012). On average, the consumption of smokeless products accounts for the largest share, 1.4 percent of the total household budget. Cigarettes follow at an average expenditure share of 1.3 percent. The cigarette budget share is even across the distribution.

The median quantity of cigarettes consumed was 152 a month per adult man age 15 years or older; the corresponding number of bidis was 348 per month per adult man (figure 5).<sup>9</sup> Assuming daily smoking, this is equivalent to an average of 5 cigarettes and 12 bidis per adult man per day. These household figures are consistent with data on individuals from the Global Adult Tobacco Survey among adult men (Barkat et al. 2012). The poorest decile consumed about 20 fewer cigarettes per adult man relative to the richest decile (132 compared with 152 cigarettes per adult man per month). However, among deciles 4–10, the median quantities were similar. One may see the opposite pattern in the case of bidis, that is, the richest consume slightly fewer bidis.

> 9 The data do not allow the identification of the household members who are smoking; so, these results should be interpreted with care.

7