rafmacalaba/data-use-mentions-extended huggingface dataset:
tertiary with data from national accounts. So, the 2009 estimates could better reflect geographic differences, while ours may probably better capture differences in educational per capita benefits. Nonetheless, there is suggestive evidence that differences in the results of this work and Higgins and Pereira (2014) may in fact reflect the actual changes of the fiscal system. Higgins and Pereira (2014) found that Brazil 2009’s fiscal system was reducing poverty and inequality until disposable income, but results for poverty are reverted when indirect taxes were accounted for. Meanwhile, Silveira and Palomo (2023) use POF 2017/2018 and find that indirect taxes have a regressive effect, but that this effect is smaller than that from POF 2002/2003. This smaller effect is in line with our finding of marginal positive impacts on poverty of fiscal policies.

20 SIMPLES Nacional is the Brazil’s simplified tax regime for small and micro firms, which also encompasses a subregime for nanoentrepreneurs named Microempreendedor Individual (MEI). Firms whose profits are larger than a given threshold are not allowed to opt in for the simplified tax regimes such as SIMPLES Nacional and MEI, and these firms were classified under the category “PJ Regular” to account for the fact that they typically face a higher tax burden.

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# **APPENDIX**

# **A Data**

## **A.1 Construction of a State Panel for Synthetic Difference-in-Difference**

States that implemented the _Pink Slip program_ could exhibit dissimilarities from other states, including neighboring ones. To mitigate this concern, we utilize a synthetic difference-in-differences approach (SDID) proposed by Arkhangelsky et al. (2021). The SDID method re-weights the control units to align their time trends with those of the treated units and subsequently applies a DID analysis to the re-weighted panel. That is, this method establishes a synthetic counterfactual state for causal estimation. Since our treatment occurs at the state level rather than the household (or individual) level, we aggregate our household-level (or individual) data to the state level and then match states and their pre-trends using a set of variables discussed below We use six variables for the household-level data. The variable “size group of household” is a categorical variable that includes groups such as one member, three members, eight to ten members, and more than 15 members. It is based on the number of members in a household. The variable “age group of household” is a categorical variable that includes groups such as households dominated by children, households dominated by grown-ups, and balanced households with seniors. The variable “occupation group of household” is a categorical variable that includes groups such as wage laborers, selfemployed professionals, entrepreneurs, and farmers. The occupation group of a household is based on the distribution of members of a household by the nature of their occupation. The CMIE classifies households into different groups based on certain rules and not just based on the occupation of the head of the household. The variable “education group of household” is a categorical variable that includes groups such as all graduates, graduates majority, and some literates. The education group of a household is based on the distribution of members of a household by their education level. The variable “gender” is a categorical variable that includes groups
_Figure 4_ - Income gains from reduction in years of life lost related to dietary risks <!-- Start of picture text --> 4.50%<br>3.85%<br>4.00%<br>3.50%<br>3.00%<br>2.45%<br>2.50%<br>1.90%<br>2.00% 1.62%<br>1.30%<br>1.50% 1.10%<br>0.91%<br>1.00% 0.74% 0.68%<br>0.44%<br>0.50%<br>0.00%<br>1 2 3 4 5 6 7 8 9 10<br>Consumption decile<br>Processed and ultra-processed foods<br>Income gains from YLL<br><!-- End of picture text --> Source: Estimates based on POF 2017/2018; Global Burden of Disease 2019. Estimates assume a price shock of 20%.

# 5.4. Medical expenditure

The third component of the model is related to the reduction in medical expenditures due to a smaller incidence of diseases related to the consumption of processed and ultra-processed foods. As was described in section 4.3, these changes are estimated using public expenditure data, which imposes several limitations on the interpretation of the results in this section. Brazil has a public and free national health care system (known by its Portuguese acronym SUS) and, while many richer households opt for private health care, most of the households at the bottom of the distribution, which as we saw are the most affected by changes in consumption following product taxation, are heavily SUS dependent.

Therefore, changes in medical expenditure cannot be understood as a direct household expenditure change, but rather as an estimate that would reflect approximate changes in medical expenses
VAT is the centerpiece. In a context of weak tax administration, the VAT with its self-monitoring feature, has become the best option for revenue generation with limited distortions. Its achievements as a revenue generator have allowed the easing of the tax burden on the formal corporate sector and the reduction of trade tax rates. The VAT revenue-productivity ratio contains information on two aspects of the VAT system: the coverage of the VAT, in terms of the share of national expenditure included in the system; and the strength and efficacy of the tax administration in preventing tax evasion. Furthermore, the VAT productivity rate indicates the ability of the state to raise additional revenues from taxes when needed. .

° See Easterly and Rebelo (1993).

.

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effects of any feasible reform in a local business environment be swamped by the forces of economic geography and path dependence? We address this issue in the context of regional gaps in manufacturing performance across India’s states. Having analyzed survey data on plants sampled from the 40 main industrial cities in the country, we find that economic geography is always a major source of the variations in productivity observed across the sample.<sup>2</sup> However, the local business environment is probably an even more powerful influence. Some cities such as Bangalore and Chennai show productivity premiums primarily due to their superior geography rather than to a better business climate. At the same time business environment explains a greater share of productivity premiums or shortfalls than geography for most of the other cities. We systematically find that inferior geography appears to be compensated by better business environment in a city’s industrial competitiveness. The more geographically advantaged is a state or a city, it seems, the more it can afford a “poor” business environment while maintaining competitiveness.

A significant source of the effect of geography on plant productivity is agglomeration (localization) economies, where business establishments gain productivity due to proximity to others in the same line of business. An even larger component is the effect of market access, defined as a proximity-weighted average of purchasing power or economic activity in potential trading partners of a location. For example, the city of New Delhi has a total factor productivity premium of 26 % in garments production over the city of Bhopal in the state of Madhya Pradesh on account of its economic geography. However, only 4 percentage points of this premium comes from greater localization economies. The balance of the (geographic) productivity premium reflects better access to markets for garments producers in Delhi. Similarly, the average garment producer in Mumbai has a 22 percentage point productivity advantage over the average producer in New Delhi for geographic reasons. Of this about 3 percentage points are due to

> inevitably mean that their removal would improve social welfare. However
are not available. Given that remote sensing data are globally available and are typically updated on an ongoing basis, this also opens the door to updating poverty maps on a higher frequency – whenever new household survey data become available. On a practical note, since the remote-sensing-based models are estimated at the survey PSU level (rather than household level), with the area-level poverty rate as dependent variable (rather than household income or consumption), experimenting with alternative poverty measures or alternative poverty lines would require returning repeatedly to the basic modeling stages of the analysis. This is not the case in the census-based approach, where the model is estimated at the household level. In this sense the remote-sensing-based approach can become rather burdensome.

It remains to be verified whether the remote-sensing-based approach is equally successful in heavily urbanized settings. Small, though not necessarily low-population, 33
# **Supplementary Materials**

# **Methods**

**<mark>Data Collection:</mark>** <mark>This paper leverages high-frequency phone surveys on COVID-19 that were implemented in Ethiopia, Malawi, Nigeria, and Uganda during the period of May-July 2020, with support from the World Bank Living Standards Measurement Study (LSMS) and the Poverty and Equity Global Practice. In each country, the phone survey aims to conduct monthly phone interviews, for a period of 12 months. The sample for these surveys is a national sample of households that had been interviewed during the latest round of the national longitudinal household survey implemented by the respective national statistical office, with financial and technical assistance from the World Bank LSMS-ISA initiative. Please see below for more information on the calculation of phone survey sampling weights.</mark>

<mark>The implementing agency for the phone survey in Ethiopia, Malawi, Nigeria, and Uganda, is, respectively, Laterite Ethiopia, Malawi National Statistical Office, Nigeria Bureau of Statistics, and Uganda Bureau of Statistics. The anonymized, unit-record phone survey data associated with each monthly survey round are made publicly available, within approximately four weeks of completion of phone interviews, through the World Bank Microdata Library, under the HighFrequency Phone Survey collection: http://bit.ly/microdata-hfps.</mark> <mark>The publicly-available data for each survey round is coupled with a basic information document, interview manual, and questionnaire for that round, which can be accessed through the following:</mark>

- <mark>Ethiopia: http://bit.ly/ethiopia-phonesurvey</mark>

- <mark>Malawi: http://bit.ly/malawi-phonesurvey</mark>

- <mark>Nigeria: http://bit.ly/nigeria-phonesurvey</mark>

- <mark>Uganda: http://bit.ly/uganda-phonesurvey</mark>

<mark>The approach to the
the gains from adopting more productive rice technologies will be substantive, and it is difficult to envision a successful path to an African Green Revolution that does not include rice.<sup>6</sup>

# **The early debate on Asia’s Green Revolution**

Before looking at the potential role for rice in an African Green Revolution, it is useful to revisit early discussions about the anticipated consequences of Asia’s new agricultural technologies shortly after their introduction, since concerns discussed then touch on topics still relevant for Africa. Though strong links between the introduction of modern varieties of wheat and rice with poverty reduction would be later documented, there were early questions about whether the new production methods would benefit large and wealthy farmers rather than poorer smallholders, and whether they would promote mechanization as a substitute for agricultural labor. At the time, some economists predicted the new varieties would lead to increased landlessness and falling rural wages, exacerbating rural poverty rather than reducing it.<sup>7</sup> The new varieties that led Asia’s Green Revolution were bred to work better with greater applications of fertilizer than traditional varieties and to work best on irrigated land (David and Otsuka, 1994). This entailed greater costs at planting time and greater capital outlays. Uncertainty over the local performance of the new varieties added to the financial risk of up-front investments, especially in the face of weak input and insurance markets. All of this seemed to favor larger farmers with better access to capital and rich enough to take on additional risk. Still, early evidence suggested that the scale of production and the wealth of the household mattered less than was supposed. For example, Hazell and Ramasamy (1991) report on an early study by Barker and Herdt (1978) on the effects of the adoption of the new semi-dwarf rices and associated technology on income and employment, based on surveys conducted in 36 villages in India, Indonesia, Malaysia, Pakistan, Philippines and Thailand. The authors found that while smallholders faced
regulation, organization of firms, and domestic rivalry. The study argues that – besides skilled labor – the availability of infrastructure systems is a key factor of production that determines the competitiveness of firms and thus entire economies.

This notion has been confirmed by a wide range of studies focusing on the role of infrastructure: Calderón and Servén (2014) review the literature on infrastructure and economic growth. Overall, both theoretical and empirical literature are concluded to find positive impacts of infrastructure development on growth and even distributive equity. Bom and Ligthart (2014) conduct a meta‐regression of 68 quantitative studies that all use a production function approach to quantify the impact of public infrastructure capital on GDP. This review included studies published between 1983 and 2008, predominantly considering developed economies countries. Through a weighted meta‐regression, the authors estimate an average elasticity of output with respect to public capital. Their assessment suggests that, on average, a 1% increase in public infrastructure capital is associated with an increase in GDP of 0.1%.

A large body of literature assesses the relationship between infrastructure and economic performance by considering a country’s infrastructure stock. In these studies, infrastructure availability is typically assessed in terms of its provided quantity, approximated for example by the total length of paved roads or phone lines in a given country. By considering the variation in infrastructure stocks across countries and time, these studies have shown a significant positive impact of infrastructure availability on various indicators of economic performance.

For instance, Straub and Terada‐Hagiwara (2010) conduct an in‐depth empirical analysis of infrastructure quality and growth, focusing on developing Asia. For this purpose, they use a range of physical infrastructure stock indicators covering telecommunications, energy, transport, and water for 102 countries, including 23 in South and East Asia, and the Pacific. They find that improvement of a country’s infrastructure base correlates significantly with higher economic growth. Similarly, using a large dataset on infrastructure stocks covering 88 developing and industrial countries and the
the heating allowance since its take-up rate is already close to 100%; according to SILC 2021, nearly all eligible households are already receiving the benefit.

In summary, while the MSA may impact all three channels, the heating allowance primarily operates through changes in eligibility, given that the transfer size is typically fixed, and program take-up is already relatively high.

**In assessing the impact of policy changes related to social assistance programs like the Monthly Social Allowance (MSA) and heating allowance, we evaluate various outcomes to gain a comprehensive understanding of the implications of the reform** . The simulations aim to comprehensively assess the proposed policy change to determine the overall effectiveness and social impact. The outcomes included in this analysis can be broken down into several key components:

- _Program Coverage_ : This refers to the number of households receiving assistance under the revised policy. When changes are made to programs like the MSA or heating allowance, it is essential to determine how many more households would become beneficiaries due to these adjustments. A higher number of beneficiaries reflects the expansion of the program's reach. Coverage is a percentage of households participating in the program by income decile (calculated as market income plus pensions).

- _Program Adequacy/Generosity_ : Adequacy assesses whether the benefits provided under the new policy are sufficient to address recipients' needs. It examines how the increased benefits can alleviate financial hardship and improve the well-being of eligible individuals or households. This is crucial for determining the real impact of the policy change on those it is intended to help. Adequacy is measured as the total transfer amount received by all beneficiary households in each decile as a share of the total income of beneficiaries in that group.

- _Impacts on Inequality_ : One key objective of social assistance programs is to mitigate income inequality. The impact on inequality assesses whether the policy changes lead to a more equitable income distribution.  It is measured as the per adult equivalent Gini index change before and after policy.

- _Impacts on Poverty Rate_ : Poverty reduction is
input costs) due to the pandemic (Figure 2, panel b) for the year 2020 as a whole relative to 2019.

Figure 2: Change in firm trade outcomes <!-- Start of picture text --> (a) Customs data: Change in value of exports and imports<br><!-- End of picture text --> <!-- Start of picture text --> 10 10<br>2<br>0 0<br>-1<br>-4<br>-10 -10 -9<br>-13 -13<br>-20 -20<br>-30 -30<br>-29<br>-40 -40<br>-41<br>-50 -50<br>Q1 Q2 Q3 Q4 Q1 Q2 Q3 Q4<br>Quarter of 2020 Quarter of 2020<br>(b) BPS data: Change in share of exports and imports<br>0<br>-5<br>-8<br>-10 -9<br>-15<br>2020 vs 2019<br>Change in exports as share of sales (p.p.)<br>Change in imports as share of inputs costs (p.p.)<br>Change in firm exports Change in firm imports<br>Change in shares<br><!-- End of picture text --> _Notes:_ The sample includes in panel a 153,079 firm-quarter observations in the left plot and 579,221 firm-quarter observations in the right plot in 20 countries and in panel b 617 exporting firms in the left bar and 1,024 importing firms in the right bar in 13 countries.

_Second_ , the pandemic had a more adverse effect on firms participating in GVCs (Figure 3, panel a). This is in line with the evidence from Lafrogne-Roussier et al. (2021) on French
estimates in Sri Lanka. A variety of features were used, including roof type, shadows, cars, and road types. In that context, feature-based prediction is more accurate, with an R<sup>2</sup> of 0.61 and a mean absolute error of 3.2 pp, as opposed to an R<sup>2</sup> of 0.39 and a mean absolute error of 5.5 pp for the CNNbased estimates.

Ayush et al. (2022) also derive several interpretable features such as trucks, maritime vessels, vehicles, aircraft, etc. They then compare predictions obtained from these features in a gradient boosting model with those obtained from a CNN trained to nightlights data, as in Jean et al. (2016). When predicting average per capita consumption across villages, the out of sample R<sup>2</sup> of the interpretable features is 0.54, as compared with 0.41 when using the CNN trained to night-time lights. While this is a lower bound measure of accuracy of the CNN, since it was trained to night-time lights, it is consistent with Engstrom et al. (2022).

Finally, as noted above, Lee and Braithewaite (2022) employ an innovative approach that first uses interpretable features to predict the DHS wealth index, and then supplements that with poverty predicted from a deep learning model. This approach, however, shows limited benefits from adding direct CNN estimates to feature-based estimates for specific countries, with increases in R<sup>2</sup> of only about 0 to 2 points across four of the five countries. The exception is South Africa, where there is an increase 13
number in the cohort relative to the total population as our measure of the relative cohort supply. Figure 3 shows the path of average wages across birth cohorts after netting out the survey fixed effect.<sup>11</sup> Starting with the oldest cohorts, average earnings rise over birth cohorts until the mid1950s when the average earnings begin to decline. The reversal is due to the declining age of the more recent birth cohorts, illustrating that we will need to control for position in the life cycle to remove the effects of age on lifetime earnings. As we demonstrate in the next section, using quadratic terms in age of the cohort or using cohort-specific fixed effects serves to correct for the age effect on earnings.

# **5. Empirical Specification**

The theory suggests that the reduced-form equation for completed schooling and earnings will depend on conditions known at the time of birth, plus changes to those variables conditioned on information orthogonal to the variables known at birth. We specify the equations for completed years of schooling j and log earnings j ) for cohort _j_ , country _c_ , and survey ρρ ρρ year _t_ as SS ln(YY j 0 j j j j j , (26) PP ρρ LL RR ρρ MM ρρ PPLL jj SSjjρρ ρρ SS = γγj + γγ0 LLEE + γγj RRUURR + γγj MMAALLEE + γγj LLEE + aaj + θθ+ ∑ + εε j + PP JJ ρρ LL RR ρρ MM ρρ PPLL pp=1 pp ρρ jj YYjjρρ Ln(YY j ) = aa. + ββ LLEE + ββ RRUURR + ββ MMAALLEE + ββ LLEE ββ ZZ + aa + θθ (27) ρρ ωω The focus on years of schooling is a matter of convenience in that we know that human capital investment will rise in every period _t_ as life expectancy for cohort _j_ , j , rises, but schooling is the most readily observable and consistent measure of human capital investment LLEE

> 11 <mark>The survey fixed effects give us an estimate of average wages net of fixed factors such
nology does not replace traditional tax enforcement strategies such as audits to expose tax evasion. Rather, technology can complement traditional enforcement by helping to analyze large amounts of data across firms to identify taxpayers at higher risk of non-compliance.

Tax officials are a crucial factor in the successful deployment of technology for taxation. If they lack adequate training or if their incentives are not well-aligned, they may avoid or circumvent the new technology. Tax authorities need to provide ongoing training to tax officials to be able to effectively use IT systems, as well as specialized training to use the electronic tax data generated for sophisticated analyses to help monitor compliance. Further, the highest levels of leadership need to establish and enforce standards of conduct and performance for tax officials to comply with the deployment of the new technology.

Whereas this paper has focused on the use of technology to administer existing taxes, an interesting area for further research is to consider how advancements in the use of technology may change the mix of tax instruments used across African countries. For example, with greater access to third-party information and employer withholding on individuals’ incomes, countries may shift to a greater reliance on income and payroll taxes, and a lower reliance on trade taxes compared to higher income countries.

Lastly, an important concern with the deployment of technology systems in taxation is the need to protect citizens from unintended consequences in terms of privacy, confidentiality, data leakages and cybersecurity. This aspect is particularly important in Africa. On the one hand, weaknesses of the legal system and rule of law present a risk of government surveillance and violation of citizens’ privacy. On the other hand, the nascent nature of data management systems within tax administration implies that such systems may be vulnerable to cyberattacks in which confidential data are accessed. As such, countries looking to expand the use of technology for taxation need to also focus on strengthening the regulatory framework to protect citizens’ privacy rights and the cybersecurity capacity of all technology systems deployed.

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