rafmacalaba/data-use-mentions-extended huggingface dataset:
and evasion by utilizing data obtained from the NTS. In addition, this paper presents findings on tax compliance from a perception survey of taxpayers and tax practitioners such as CPAs, tax accountants, attorneys, and tax specialists in corporate or individual businesses. Lastly, some concluding insights and reflections are presented for tax authorities contemplating ETI with the benefit of hindsight from Korea’s ETI experience.

# **II. ETI EXPERIENCES ACROSS COUNTRIES**

## **1. What Is ETI?**

An invoice is a commercial document that a business issues to a customer with the agreed price, quantity, taxes paid or owed, and other information pertaining to a transaction. It constitutes mutual recognition of the occurrence of a transaction and serves important accounting purposes. An invoice plays a particularly important role in an invoice-credit VAT regime that determines the VAT due by deducting VAT paid on a purchase from VAT payable upon a sale. Businesses have an incentive to obtain an invoice for each purchase from their suppliers because they can claim the input tax credits available to them. Mindful of VAT claims by their customers, businesses also have an incentive to report their sales to the tax authority. Invoices therefore provide a transaction “trail” that encourages self-enforcement among businesses.

An electronic tax invoice or ETI is an electronically produced invoice containing transaction information much like a paper invoice that businesses issue to their customers. ETIs are widely used nowadays, but their methods and formats vary from country to country. In recent years, the EU has relaxed its stance on “e-invoice” formats in order to reduce barriers to the use and acceptance of e-invoicing, while some non-EU countries such as Chile, Korea, Mexico, and Taiwan, China, have opted for standardized formats such as structured formats using extensible markup language (XML).

The EU VAT Directive defines electronic invoice as “an invoice that contains the information required in this Directive,<sup>4</sup> and which has been issued and received in any electronic format.”
Policy Research Working Paper 9463

# **Abstract**

This study aims to provide a quantitative and integrated analysis of long-term structural transformation and labor productivity growth in Malaysia. Using data from the Department of Statistics Malaysia from 1987 to 2018 and decompositions that take account of the static and dynamic efficiency gains from labor reallocation, it documents that Malaysia has undergone structural transformation from an agriculture-driven to a services-driven economy. However, in contrast to common perceptions, the country’s impressive growth in output per capita over the past three decades can largely be attributed not to its structural transformation but instead to sustained growth in within-sector labor productivity. At 3 percent, the contribution of between-sector reallocation of labor to growth in output per capita in Malaysia has been relatively low. Accordingly, together with efforts to spur the more productive reallocation of labor across sectors and positively affect the employment rate, the main policy challenge for Malaysia going forward will be to achieve sustainable labor productivity growth within various sectors.

This paper is a product of the Social Protection and Jobs Global Practice. It is part of a larger effort by the World Bank to provide open access to its research and make a contribution to development policy discussions around the world. Policy Research Working Papers are also posted on the Web at http://www.worldbank.org/prwp. The authors may be contacted at aabdurrahman@worldbank.org and aschmillen@worldbank.org.

_The Policy Research Working Paper Series disseminates the findings of work in progress to encourage the exchange of ideas about development issues. An objective of the series is to get the findings out quickly, even if the presentations are less than fully polished. The papers carry the names of the authors and should be cited accordingly. The findings, interpretations, and conclusions expressed in this paper are entirely those of the authors. They do not necessarily represent the views of the International Bank for Reconstruction and Development/World Bank and its affiliated organizations, or those of the Executive Directors of the World Bank or the governments they
**Table 2:** Female labor force participation and Gini index (fixed effects panel analysis, w/o high income countries).

|Dep. var.: Gini index (1=100)|||||
|---|---|---|---|---|
||(1)|(2)|(3)|(4)|
|Female LFP (% of female population ages 15+)|0.317**<br>[0.145]|0.586***<br>[0.178]|0.747**<br>[0.328]|0.652**<br>[0.291]|
|Female LFP squared|-0.003**<br>[0.001]|-0.005***<br>[0.002]|-0.008***<br>[0.003]|-0.008***<br>[0.002]|
|Services value added (% of GDP)||0.212***<br>[0.077]|0.188***<br>[0.064]|0.197***<br>[0.055]|
|Exports of goods and services (% of GDP)||0.078*<br>[0.043]|0.133**<br>[0.062]|0.133**<br>[0.058]|
|Imports of goods and services (% of GDP)||-0.031<br>[0.026]|-0.038<br>[0.061]|-0.076<br>[0.061]
not available for the whole 1965-95 period. The most complete are sixteen series for wheat based on different origins and destinations published in the FAO yearbooks, including wheat freights from US gulf ports to Japan and Rotterdam, and to I]ndia in some years( see Annex 1). These have been used to estimate freight rates for wheat and also as a basis for the estimates for some of the other crops. For other commodities, actual freight rates were available from Indian sources for a few years, and we used the international wheat freight series or other series to index these forwards and backwards to other years. Various other approximations and calculations are explained in the individual background papers. Among the complications that should borne in mind in interpreting these estimates is that for many commodities in most years they are hypothetical, since there was no trade actually occurring. However the level of freight rates depends among other things on the volume of trade and the efficiency of ports. In using published international freights as a basis for estimating Indian freights, we generally assumed implicitly that if trade had occurred, it would have been in large ships in large volumes, and that port capacities would have been adjusted accordingly without, for example, port bottlenecks and long waiting times for ships which would be reflected in freight rates 7

# _Port costs_

Estimates of port costs -mainly the cost of unloading imports or loading exports, but also other expenses such as customs clearance and storage- were made in the original commodity studies, but usually for one or a few years in the 1 980s only. For a number of commodities there were no exports or imports, and it was assumed that port costs would be the same as for other similar commodities. Partly for the same reasons, we were also unable to find actual port costs for the 1970s and earlier. To project the observed port charges forward to 1995 and back to 1971 we used the public sector emolument index, on the reasoning that this would approximate labor costs in the ports, which are
Policy Research Working Paper 7574

# **Abstract**

Evidence on the impacts of a large-scale expansion in preprimary education is limited and mostly circumscribed to high- and middle-income nations. This study estimates the effects of such an expansion on progression in primary school in rural communities in Guatemala, where the number of pre-primary schools increased from about 5,300 to 11,500 between 1998 and 2005. Combining administrative and population census data in a differencein-differences framework, the analysis finds that access to pre-primary education increased by 2.4 percentage points the proportion of students that progress adequately and attend sixth grade by age 12. These positive although limited effects suggest the need for complementary actions to produce substantial improvements in adequate progression.

This paper is a product of the Trade and International Integration Team, Development Research Group. 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 authors may be contacted at pbastos@worldbank.org.

_The Policy Research Working Paper Series disseminates the findings of work in progress to encourage the exchange of ideas about development issues. An objective of the series is to get the findings out quickly, even if the presentations are less than fully polished. The papers carry the names of the authors and should be cited accordingly. The findings, interpretations, and conclusions expressed in this paper are entirely those of the authors. They do not necessarily represent the views of the International Bank for Reconstruction and Development/World Bank and its affiliated organizations, or those of the Executive Directors of the World Bank or the governments they represent._ Produced by the Research Support Team
2008). That begs the question then of why there are two neighborhoods of seemingly noninteracting firms found in one city. The answer must be that firms benefit more generally from the urban scale.

In Table 8 we address this issue for household income data. All columns control for citywide ln PD. Local density and overall measures are correlated, so if the local density measures matter that will reduce the elasticity (0.59 in col 1) of ln city PD. This is the case except in column 4 where the local density measure is insignificant. While the coefficients on ln city PD decline when we include key local measures, they are still very large, indicating large marginal returns to overall city density. Columns 2-5 each experiment with a different measure of local economic density. Although we have argued that a simple PD measure works as well or better than anything else for a citywide measure, that does not mean at the local level it is the best measure. In column 2 the local measure is ln (local PD); in 3 it is ln (local PPD); in column 4 it is ln (own cell population), and in 5 it is the simple de la Roca-Puga measure (A in eq. 3). The ln (own cell population) has an insignificant effect. In all other columns, local density measures have strong significant effects, with little to choose between them in terms of magnitudes. Magnitudes are all about 0.14, much smaller than the elasticity of about 0.45 for overall city density. R-squared’s are very similar across columns 2, 3 and 5, although the column with ln local PPD does have the highest R-squared. Horseraces suggest ln local PPD with a coefficient (s.e.) of 0.135 (0.071) dominates ln local PD (0.038 (0.068)) and ln local PPD with a coefficient of 0.141 (0.058) dominates ln local A (0
Costa Rica, the Dominican Republic, Ecuador, El Salvador, Guatemala, Haiti, Honduras, Mexico, Nicaragua, Panama, Paraguay, Peru, and Uruguay.<sup>15</sup> The data set on poverty lines gathered through official sources is the major contribution of this paper to the estimation process of regional comparable poverty estimates. The pioneering work of Ravallion, Datt, and van de Walle (1991) gathered a compilation of 33 non-official national poverty lines for the whole world, while Ravallion, Chen, and Sangraula (2009) used data on 88 poverty lines extracted from the World Bank’s program of country _Poverty Assessments_ that have been carried out since 1990 worldwide. In contrast, this paper comprises the most comprehensive and up-to-date database on official poverty lines for LAC countries. We collect more than 80 _subnational_ urban poverty lines distributed in 18 countries in LAC, accounting for regional disparities within countries by capturing subnational heterogeneities in the standard of living.

_3.2 Income distribution from harmonized household surveys_ In order to estimate poverty measures that are comparable across countries, we need to increase the cross-country comparability of the welfare measures. Therefore, the second source of information is the SEDLAC data set,<sup>16</sup> which is used as the primarily source of comparable welfare aggregates across the LAC countries and represents approximately 518 million individuals, or 85 percent of LAC’s population in 2013.

The main objective of this comprehensive data set of household surveys is to increase crosscountry comparability of a range of socioeconomic measures, including household total income, from more than 300 household surveys within 18 countries in the LAC region from the 1970s to the present. Following the practice of most countries in the region, in this paper we measure poverty based on household per capita income at the individual level. Given that the main purpose of the SEDLAC project is to enhance cross-country comparability of household surveys, these

> Economic Research (IPEA, for its acronym in Portuguese) several years ago, which are updated
who did the other major study on income distribution shows a decline in income inequality from 0.51 in 1960 to 0.37 in 1980 using consumer surveys. Studies by Collier (1983) and Bevan et al (1988) also provide estimates of changes in income composition and income inequality. Collier provides income inequality and land inequality in terms of different crop growers and regions, while Bevan _et al_ provide intrarural and inter-temporal income distribution.

Another issue of interest has been the general distribution of incomes between urban and rural areas. Bevan _et_ al(1988) indicated using nominal expenditure differentials, that between 1950 and 1980, there was a considerable expenditure differential in favor of urban residents because food was cheaper in the rural but manufactures were dearer. Between 1952 and 1954 per capita urban expenditure were 81 percent higher than rural per capita expenditures. It is worth noting that according to the present analysis, in 1985/86 urban per capita expenditures were only 32 percent higher than rural per capita expenditures, and in 1992/93 it was 13.5 percent higher. Faruqee (1993) argues that the gap which was in favor of urban areas at 58 percent in 1980/81 reduced to 8 percent in 1985/86 and reversed to be in favor of rural areas after 1986.

Most of the discussion on evolution of income (or expenditures) distribution included in here will focus on two surveys, the 1985/86 and 1992/93 National Consumer Survey. The first presentations in Table 4.1 below show the mean per capita expenditure distribution of households by expenditure deciles for 1985/86 and 1992/93. Table 4.1 shows that the bottom five percent had a large declining mean per capita expenditure from N 118.11 to N 70.24, between 1985/86 and 1992/93. The table also shows that households in the second five percent had average per capita expenditures of N 175.22 in 1985/86 and a mean expenditure of N 140.5 in 1992/93
of this paper comes from the coresidency restriction; most of the surveys suffer from sample selection due to coresidency used to define household membership. As noted before, this has been a strong discouraging factor for researchers worried about rejection by their peers, journal referees and the editors.

The recent economics research on intergenerational economic mobility in developing countries includes Behrman et. al. (2001), Hertz et al. (2007), Binder and Woodruff (2002), Thomas (1996), Lillard and Willis (1995), Lam and Schoeni (1993), Emran and Shilpi (2011, 2015), Bossuroy and Cogneau (2013), Maitra and Sharma (2010)). Most of the studies on economic mobility in developing countries rely on education and occupation as markers of economic status, because reliable data on income for long enough time periods to calculate permanent income are not available.<sup>13</sup> Most of them also use data selected nonrandomly due to the residency requirement for household membership. There is, however, no uniformity in the definitions of ‘household’ across different surveys, although all are concerned with ‘living together’, ‘eating together’, and sometimes with ‘pooling of funds’ (Deaton (1997)). Examples of household surveys that usually include coresidency as a defining criteria include Household Income and Expenditure Survey (HIES), Demographic and Health Survey (DHS), and Living Standard Measurement Survey (LSMS). There are some household surveys which include limited information on the parents of household head and spouse, but do not include the nonresident children of the household head. Hertz et al. (2007) use household surveys from 21 developing countries (10 Asian, 4 African, and 7 Latin American) and 8 formerly Communist countries where household surveys provide information on household head’s parents, but do not include the nonresident children.<sup>14</sup> When non-resident children are excluded from the survey, it results in truncation
businesses, restricting movement, or imposing curfews (Hirvonen et al., 2022). In Malawi, the President declared a state of disaster on 20 March 2020, which included closing schools and limiting the size of public gatherings. A stay at home order was issued in April. However, this order faced legal challenges, which culminated in the High Court barring the regulation and preventing the stay-at-home order from going into effect, leaving daily economic activity largely intact (Josephson et al., 2021). Nigeria’s response primarily occurred at the state level. Most Nigerian states closed schools and suspended large gatherings by 24 March 2020 and suspended inter-state travel on 23 April. While non-essential shops as well as restaurants were ordered to close, the government’s attempts to impose these closures along with curfews, social distancing, and self-quarantine, were largely ignored, meaning daily economic activity was relatively unchanged (Jacobs and Okeke, 2022). The government lifted the closure order less than a month later. Compared to lockdowns in China, Europe, and the United States, the closures of businesses and the restrictions to daily activities in Ethiopia, Malawi, and Nigeria were substantially less strict. This is important since it means that households in these countries were less constrained in pursuing livelihood opportunities than those in regions of the world with government imposed lockdowns.

To account for the variation in COVID-19-related restrictions over time, we use _Our World in Data_ ’s COVID-19 Government Stringency Index in some of our empirical specifications (Mathieu et al., 2020). The index considers nine metrics to calculate daily scores for each country: school closures; workplace closures; cancellation of public events; restrictions on public gatherings; closures of public transport; stay-at-home requirements; public information campaigns; restrictions on internal movements; and international travel controls. The stringency index is calculated as the mean score of the nine metrics, each taking a value between 0 and 100. A higher score indicates a stricter regulatory regime. To match these daily data to each round
technological inputs of modern activities allows for productivity spillovers into the broader economy. Rodrik also emphasizes that these modern activities are largely tradable products, although the broader benefits of tradables is less dependent on whether trade actually takes place.<sup>10</sup> As resources move from traditional activities towards modern, economy wide productivity increases (see Rodrik 2009b, Stiglitz et al 2001). This can take place in agriculture, manufacturing, or services.

The distinction between traditional and modern activities will play a critical role in the remainder of our analysis. Therefore, before we analyze the composition of Malaysian production, we first offer our defining characteristics for “modern” and “traditional” production/trade separately for goods and services.

# **III.  DEFINING MODERN AND TRADITIONAL GOODS AND SERVICES**

There are two criteria that we can use to classify industrial activities (specifically, manufacturing) as either modern or traditional. The OECD classifies manufacturing industries according to several categories of technological intensity using the ratio of R&D expenditures to total value-added. The division of industries into these categories was made after ranking the multi-year average R&D ratios against an aggregate OECD average.<sup>11</sup> This distinction was used most recently in the 2009 Industrial Development Report published by the United Nations Industrial Development Organization (UNIDO). The UNIDO publishes a listing of three-digit SITC codes that correspond to four technological classifications of trade: resource-based, lowtechnology, medium-technology, and high-technology industries. Using the UN COMTRADE database, we can access the commodity-level trade of individual countries and separate it by this technological classification. Calculating the share of high-technology trade with respect to the total gives us a measure of the technological intensity of trade for that country. In addition, UNIDO also provides an equivalent classification based on ISIC codes for production-level analysis; however, the available data on the production side is more limited.

An alternative measure of the _sophistication_ of goods exports is provided by Hausmann, Hwang, and Rodrik (2007).
benefit analysis; we also compute and report these results in section 5.4. In addition, once we have specified a proper likelihood on the data, it is natural to consider jointly modelling the potential outcomes to get more detailed causal inference on the distribution – of treatment effects in the style of Imbens and Rubin (2015) we pursue this as an extension to our main analysis (additional details and results provided in section 5.4).

We do note that the approach described above differs from the way that Bayesian statisticians or analysts in other fields would tend to approach this same data. Modern Bayesian data analysis differs from the Frequentist econometric approach in more respects than just the addition of priors; in particular, Bayesian analysis typically uses richer likelihood models that are tailored to the specific functional forms of the outcome variables in question, since much is typically known about them (e.g. productivity is bounded below at zero and thus likely right-skewed, export probability is binary, etc). However, we pursue a Bayesian analysis using Gaussian likelihoods because in the absence of priors these likelihoods deliver the OLS estimates of the regression coefficients, making it easier to compare the frequentist and bayesian results and to understand the role of our informative expert and firm priors.<sup>9</sup>

# **5 Results**

## **5.1 Take-up and usage**

Take-up and usage of the program was high, especially given the requirement for firms to pay US$1,000-$2,000 to participate. 88 of the 100 firms assigned to treatment are recorded in the administrative data as starting the intervention, and 83 have positive hours of consulting recorded. Figure A1 shows that this take-up rate is high compared to the medians of the prior distributions for take-up of the participating firms (58%), academics (63%) and policymakers (73%), and in line with the mean of our pre-specified literature prior (80%). Moreover, of