rafmacalaba/data-use-mentions-extended huggingface dataset:
2 international convergence. In addition, returns to education and work experience, i.e. the rates of human capital accumulation at work, may vary across sectors (Islam et al., 2019). Likewise, agglomeration economies may also vary by sectors (Venables, 2017; Burger et al., 2022). That may also be the case for congestion externalities. Finally, international trade affects endogenous innovation and growth (Melitz and Redding, 2021).

We first establish that, for a given population size and a given level of urban economic development, cities dramatically vary in their sectoral composition across the world. Using census data for about 65 countries, we obtain the sectoral composition of 7,000 agglomerations comprising three-fourths of the world’s urban population. We classify them as “production cities”, “consumption cities”, or “neutral cities”, depending on their employment share of manufacturing and tradable services. We also use our classification to highlight novel stylized facts related to the global distribution of urban employment and show that cities, not just countries, experience different patterns of structural change.

We extend the theoretical analysis of GJV16, who show that countries can urbanize because of _industrialization_ broadly defined (which includes tradable, or “industrialized”, services) or because they export _natural resources_ . An increase in resource export earnings raises incomes. The resource export earnings are used to import food and other tradable goods whereas the higher demand for urban non-tradables is met by an increase in labor in that sector. Since the rural sector contracts, the country urbanizes. However, urbanization generated by resource rents differs from industrialization-led urbanization in that cities will have different employment shares of tradables and non-tradables.

We consider two other paths leading to consumption cities. In GJV16, resources include fuels, mineral products, and a few high-rent cash crops, which require little labor. Yet, many countries export _agricultural products_ , including food crops. Their production generates rents. However, it also requires rural labor, leading to ambiguous
based on remote sensing (Deininger & Xia 2018), an area where significant advances have recently been made through use of survey-based training data (Burke _et al._ 2020a) offers considerable promise.

Second, the impact of land title on small farms has been explored by numerous studies (Lawry _et al._ 2016). ‘Stylized facts’ from this literature are that titling helps to increase investment (Fenske 2011), transferability (Deininger & Jin 2006), and -if women’s rights are documented- female empowerment (Deininger _et al._ 2008; Ali _et al._ 2014). However, credit effects are rare because (i) banks will accept land as collateral only if registries are kept up-to-date;<sup>5</sup> and (ii) even if titled properties could realistically be foreclosed,<sup>6</sup> fixed costs of loan origination may limit positive effects to large farms (Carter & Olinto 2003). The finding that, for Zambian large farms, title affects neither investment nor credit access is thus at variance with the literature. It has been attributed to limited demand, i.e., land in Zambia being viewed as a store of wealth rather than a productive asset (Sitko _et al._ 2014) or methodological differences.<sup>7</sup> We argue that supply side issues related to legal value of land documents, the quality of the information they contain, and the states’ capacity for (and cost of) enforcement are equally important and that better measurement of these aspects and rigorous evaluation of the extent to which they affect title demand and tangible outcomes will be key to understand the land sector’s institutional performance and identification of ways to improve it.

Finally, a large literature on structural transformation has shown that land (rental) market transaction cost may impede farm growth (Adamopoulos & Restuccia 2014) and occupational diversification (Chen 2017). In principle, eliminating such costs could have
not pay much into the system as of 2017, as personal income taxes have a high exemption threshold and sales taxes only apply to a selected number of goods that are more likely to be consumed by richer households. Due to the lack of direct transfers, the net cash position is negative for poor households, though the magnitude is very small. Simulations suggest that recent reductions in personal income taxes and the planned introduction of the GST would alter these distributional impacts. With the reduction in PIT, direct taxes have become slightly more progressive; however, the inequality-reducing impact is slightly dampened than before. The GST as a broad-based tax likely leads to an increase in indirect taxes paid by households across the distribution, including those in the bottom 40 percent. At the same time, the GST is much less progressive than the existing sales tax. While the GST is poverty-increasing as a result, the poverty rate reverts to levels similar to the baseline in 2017 after considering the growth of income/GDP during this period. This suggests that the negative welfare impact of the new GST is likely to be temporary. More broadly, some of the additional revenues raised from the GST could effectively be used to offset the temporary deterioration in household welfare, such as through direct transfers to poor households.

This study shows that fiscal incidence analysis tools can provide important insights into the distributional impact of the fiscal system, including major fiscal reforms. While Bhutan has achieved significant poverty reduction in the past, effective fiscal policies can help further accelerate progress.

The exercise faced severe data challenges. In particular, the household survey did not contain information on incomes and information on labor market activities was also not available. Additional limitations in data availability prevented the use of other methodologies employed in the literature, such as survey-to-survey imputations. Instead, this study uses a sophisticated consumption-based modeling approach to estimate individual-level market income and simulate direct taxes and social contributions at the same time. This is the first such study to our knowledge and could be a useful example for low-income
questions on income received in the previous calendar year. We use the official poverty lines for the US, which gives a poverty rate of 12.3% in 2018 (Semega et al., 2019).

For Mexico, we use the National Survey of Occupations and Employment (ENOE). This is a trimonthly survey applied to a representative household sample in Mexico. The survey aims at providing statistical information on the population’s occupational and substantive sociodemographic characteristics at the national level. We do the currency conversion at Purchasing Power Parity (PPP).<sup>9</sup> However, we also allow for extra costs of living in the U.S.not fully reflected in the PPPs, such as higher housing costs.

Using these data, we can now explore the impact of creating a market for WPs through a series of simulations. Recognizing the parameter uncertainty (especially on migration costs) we consider a seemingly wide range of parameter values in our model, though with an “inner range” of what appear to be the most plausible values based on what we know. The aim is not exact calibration but rather to get an idea of the scale and economic feasibility of this policy. In particular, we ask whether the observed distributions of earnings in the two countries, and likely frictions to migrating, are such that Mexican workers would be willing to purchase temporary WPs at prices that would attain something like recent basic income proposals for the U.S. U.S. worker rents out her work permit for a year if offered a price exceeding the product of the replacement rate ( ) and her current yearly wage ( UU ); the total number of U.S. ii pp citizens willing to rent out their work permit is then given by: rr ww = ∑ U UU (4) mm ss mm=1 mm NN 1[rr. ww < pp] In setting the replacement rate, we draw on the surveys that have been done to determine what share
>60<br>-40 60 -40<br>40<br>-60 40 -60<br>-80 20 -80 20<br>-100 0 -100 0<br>03/01 05/01 07/01 09/01 03/01 05/01 07/01 09/01<br>Stringency of<br>Mobility around<br>Period of interview lockdown<br>transit stations<br>restrictions<br>Google mobility Stringency of Google mobility Stringency of<br>index (baseline = 0) lockdown measures index (baseline = 0) lockdown measures<br>Google mobility Stringency of Google mobility Stringency of<br>index (baseline = 0) lockdown measures index (baseline = 0) lockdown measures<br>Google mobility Stringency of Google mobility Stringency of<br>index (baseline = 0) lockdown measures index (baseline = 0) lockdown measures<br>Google mobility Stringency of Google mobility Stringency of<br>index (baseline = 0) lockdown measures index (baseline = 0) lockdown measures<br><!-- End of picture text --> Data from Google (2020) for the mobility trends and Hale et al. (2020) for the lockdown restrictions. The mobility trends are smoothed using 7-day moving averages.

39
CAMEROON’S INFRASTRUCTURE: A CONTINENTAL PERSPECTIVE dependent on natural resources, it will be benchmarked against other resource-rich economies in Africa as well as against middle-income countries. Detailed comparisons will also be made with immediate regional neighbors in Central Africa.

Several methodological issues should be borne in mind. First, because of the cross-country nature of data collection, a time lag is inevitable. The period covered by the AICD runs from 2001 to 2010. Most technical data presented are for 2008 (or the most recent year available), while financial data are typically averaged over 2001–06 to smooth out the effect of short-term fluctuations. Second, in order to make comparisons across countries, indicators were standardized so that analyses were done on a consistent basis. This means that some of the indicators presented here may be slightly different from those that are routinely reported and discussed at the country level.

# Why infrastructure matters

Between 2002 and 2009 Cameroon’s gross domestic product (GDP) grew about 3 percent per year. This economic performance falls short of the 7 percent mark needed to make a significant impact on poverty reduction and below the average growth rate of 6.2 percent for Sub Saharan Africa.

The overall contribution of telecommunications, electricity, and roads to Cameroon’s per capita growth between 2000 and 2005 was 1.05 percentage points, mostly attributed to a faster accumulation of infrastructure assets than to improvements in infrastructure quality. As elsewhere, the ICT sector was responsible for most of the contribution, adding 1.26 percentage points to the per capita growth rate. The power sector held back per capita growth by –0.28 percentage points (figure 1a). The dead weight of power on Cameroon’s economy is close to three times the negative effect that power deficiencies represented for Africa as a whole during the same period.

Infrastructure’s contribution to economic growth in Cameroon was higher than in other Central Africa countries (figure 1a). But in the larger Sub-Saharan African context, infrastructure development led to faster growth
Economywide trends in TFP over the period 1990-2015 reveal that South Africa has mostly lagged behind its developing country peers, and recently even experienced a contraction in TFP growth. By exploring firm-level dynamics using tax administrative data, we are able to gain further insights into the structural changes observed on an aggregate level. This allows for a better understanding of firm, employment and productivity dynamics.

The paper is structured into five sections. Section 2 provides a brief overview of the SARS administrative tax data. Section 3 discusses where formal wage workers work in terms of firm characteristics such as size, age and economic activity. In Section 4 we look at which type of firms are creating the most jobs. Section 5 focuses on dynamics between productivity growth and jobs and the efficiency of factor allocation. Section 6 sets out the regression analysis and section 7 provides the results. Conclusions and policy implications are discussed in section 8.

5
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|In Turkey, [Ikkaracan et al (2015) evaluated ex-ante a policy intervention that would increase child care<br>Our paper contributes to the ex-ante simulations literature on child care in two different ways: (i)<br>firstly it<br>calculatingthecapacityexpansion;and(ii)secondly,itusesdataonthechildcaresectorofadeveloping|
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Policy Research Working Paper 10678

# **Abstract**

This paper examines the distributional effects of fiscal policy in Grenada. Using data from the 2017–18 Living Conditions and Household Budgets Survey and following the Commitment to Equity analysis framework, the paper estimates the effects of fiscal policy interventions on inequality and poverty. It analyzes the distributional incidence of direct and indirect taxes, direct transfers provided by social transfers and school feeding programs, and in-kind transfers generated by public services in health and education. The results show that Grenada has a tax system that is neutral on the value-added tax side and progressive on the personal income tax side. Furthermore, direct transfers make a modest contribution to poverty reduction and are almost neutral in their distributive impact. The results contribute to the understanding of who bears the burden of taxation and benefits from transfers and of how Grenada’s fiscal system can improve its redistributive effect.

This paper is a product of the Poverty and Equity Global Practice and the Macroeconomics, Trade and Investment 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 gcanavire@worldbank.org; ranli@worldbank.org; Chevanne.britton@gmail.com; friosa@gmail.com; wjimenezpozo@gmail.com; sgranadosibarra@worldbank.org; and ggomezaliaga@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
- 15 -

estimated that policies and institutions accounted for the largest share in the increase in the 9<sup>th</sup> /1<sup>st</sup> earnings decile ratio, more than technological change and much more than globalization. Declining real minimum wages are one of the institutional changes driving this inequality. This has been established for developed countries for a number of years—for example, DiNardo, Fortin, and Lemieux (1996) find that a falling real minimum wage explains a substantial proportion of the increase in U.S. inequality in the 1980s. Now research for developing countries is making the same association. For example, Bosch and Manacorda (2010) analyzed household data in urban Mexico from 1989-2001 and found that the steep decline in the real minimum wage accounted for a substantial part of the growth in inequality over this period.

But do the equalizing wage effects of higher minimum wages translate into poverty reductions? Indeed, advocates often present minimum wages as an anti-poverty tool. Proponents argue that, not only do minimum wages raise the earnings of low-wage workers but, because of intrahousehold transfers and the link with many social benefits, they have broader ripple effects (Eyraud and Saget 2008). On the other hand, many economists are more skeptical, partly because of any disemployment effects but even more because of the targeting of minimum wage rules—i.e., many workers covered by minimum wage legislation are not in poor households while the poorest households often do not have members covered by minimum wage rules. In a theoretical exposition, Fields and Kanbur (2007) show that the poverty impacts of the minimum wage are indeterminate and depend on the degree of poverty aversion, the elasticity of labor demand, the relationship between the minimum wage and the poverty line, and the extent of income sharing within the household.

The empirical evidence on the minimum wage-poverty relationship in developing countries largely comes from Latin America. The conclusions vary. Some find that increases in the minimum wage do
worst-off households during active conflict is most effective in staving off the adverse outcomes associated with lifelong impacts (e.g., Kurdi et al. 2019). However, the lack of updating despite significant changes in both food security and conflict in the country suggests that official early warning systems need to be updated more regularly.

Second, the lack of a stronger and more immediate improvement in food access suggests potential improvements to data collection during humanitarian crises. In addition to monitoring traditional metrics that focus wholly on food access, capturing important categories of non-food consumption and access to basic services in high-frequency surveys could help to better understand how in-kind food assistance, cash transfers, and other types of humanitarian assistance might impact household wellbeing during humanitarian crises. In many crises, including in Yemen’s, food is one of many urgent needs (e.g., OCHA 2017). Although it has long been recognized that the impacts of in-kind assistance might not increase consumption of the goods being provided to beneficiaries (e.g., Behrman and Deolalikar 1989, Jensen and Miller 2011, etc.), it is possible that the existence of many overlapping and potentially competing deprivations could make the situation more likely. Although some assessments, such as the 2017 IPC announcement, do take a wide variety of indicators into account and captures some of these other urgent needs (e.g., Maxwell et al. 2020), more systematic measurement of other important dimensions can potentially help with better targeting of assistance in Yemen and future crises.

The rest of the paper is organized as follows. Section 2 discusses the official 2017 IPC announcement in Yemen; section 3 discusses the data; section 4 presents important summary statistics of food assistance and food access; section 5 presents the empirical strategy and how food assistance and food access changed following the 2017 IPC announcement; section 6 discusses potential reasons why the increase in the share of households receiving assistance might not have had a larger or more immediate impact on food access; and section 7
<!-- Start of picture text --> 80<br>71 70<br>68<br>66<br>63 64<br>60<br>40<br>37<br>20 20 21 23 24<br>16<br>0<br>Agriculture Manufacturing Retail Other Accomm & Knowledge<br>services food serv. intensive<br>services<br>Low digital readiness firms (pre-pandemic)<br>High digital readiness firms (pre-pandemic)<br>Probability of increasing use of digital tech.<br><!-- End of picture text --> Figure 5: Predicted probability of digital response to COVID-19 by digital readiness (pre-pandemic) and sector

# **4.2**

The increase in the use of digital technologies during the COVID-19 pandemic is correlated with firm size. Large firms performed better than small firms with respect to the uptake of digital solutions. The likelihood of starting or increasing the use of digital technologies ranged from 34% for micro firms<sup>11</sup> to 43% for small firms, 50% for medium-sized firms, and 52% for large firms (Figure 6). These differences across firm size groups are starker when looking at investments in technology adoption.The likelihood of investing in digital solutions ranged from 20% for micro firms to 49% for large firms. This is not surprising as investments in new digital solutions often entail high fixed costs.

The World Bank (2022) using more granular data for Malaysian firms identifies that the digital divide across firm size is not only present in the extensive margin. Medium firms invested 3 times more as a share of their sales than small firms, and large firms invested five times more. As will be shown in the next subsection, not only the size but also the focus of such investments has been different.

> 11Only for this analysis micro firms are included for the subset of countries with available data

10
EDD are constructed using exporter-level customs data, which cover the universe of annual exporter transactions.

Information on financial structures, including the importance of the banking sector relative to the stock market and the importance of foreign banks relative to domestic banks, is obtained primarily from the Global Financial Development Database (GFDD).<sup>6</sup> We complement the GFDD with data from Burger et al. (2015) and Yibin et al. (2013) on the importance of bank credit relative to bond market capitalization. Further, we draw relevant information from a variety of other databases and sources, including the analyses in Maskus et al. (2012) and Klapper et al. (2006) to construct industry-level measures of external financial dependence, the World Bank Banking Supervision Survey on the supervision, monitoring, and regulatory restrictions on banks, the database in Laeven and Valencia (2013) on the occurrence of financial, banking, and external crises, and the analyses in Nunn (2007) and Rauch (1999) to obtain measures of contractual and information complexity of products. We provide further details on measurement and data sources below.

To identify the effect of financial structures on export dynamics, we rely on the established strategy proposed by Rajan and Zingales (1998) which exploits heterogenous financial dependence across industries based on technological factors inherent to the production process. Specifically, in the baseline tests for each outcome we estimate the following regression: _Exportist_ = _β_ 1 _FinStructit × ExtFins_ + _β_ 2 _FinDevit × ExtFins_ + _Dit_ + _Dst_ + _εist_ (1) where _i_ is country, _s_ is ISIC 2-digit industry and _t_ is year. _Exportist_ are variables from the Exporter Dynamics Database that capture the size, dynamics and concentration of the export sector at the country-industry-year level, and _FinStructit_ is the measure of financial structure, capturing the relative importance of banks vis-`a-vis capital markets or the relative importance of foreign banks vis-`a-vis domestic banks. _ExtFin_ measures the industry’s external financial dependence. _FinDevit_ is a