rafmacalaba/datause-encoder-data huggingface dataset:
Whether one would see the DPE in a growing developing country is a moot point. It can be argued that such economies are characterized by factor-market imperfections and surplus labor, dulling the Balassa-Samuelson mechanism. Productivity increases for non-traded goods could also dull the effect. In addition to the possibility of a DPE, one can also expect that there will be measurement errors confounding the PPP comparisons.

A better understanding of the economics of PPPs changes should help in estimating PPPs for the (many) years for which there is no ICP round, with implications for the extent of the data revisions needed at each new ICP. There are two distinct problems of missing data in estimating PPPs; the first is that some countries chose not to participate in the ICP’s price surveys, while the second is that there are long gaps between those surveys. Existing data compilations (such as the World Bank’s _World Development Indicators_ ) use extrapolations based on non-ICP data to fill in these missing PPPs, but very different types of data are used. The extrapolations to deal with the first problem are based on GDP per capita at market exchange rates, exploiting the static Penn effect. By contrast, in addressing the second problem, the PPPs for non-benchmark years are estimated by re-scaling the PPP from the most recent ICP round according to the inflation rate (GDP deflator for the GDP PPP, and Consumer Price Index for the consumption PPP) in the country in question relative to the US inflation rate. The reliability of this method is unclear. While in theory, a suitable inter-temporal price index could deliver reliable extrapolations, it is far from obvious how well the methods used in practice perform, notably in reflecting the changes in the relative price of non-traded goods in growing economies.<sup>5</sup> However, as this paper will show, there is another possible method for the inter-temporal extrapolations, consistent with how the first problem of missing data is dealt with. If the relationship between price levels and mean income—the static Penn effect that is used to deal with the cross-sectional missing data—also holds over time then this could be exploited in the dynamic extrapolation methods between ICP rounds, thus offering scope for reducing the need for the large data revisions often implied by each new round of ICP data.<sup>6</sup>

> most common parametric test entails regressing the log of the price level index on the log of GDP per capita in $US at market exchange rates.

> 5 Neither the underlying prices nor the aggregation methods are typically the same between the national deflators and the PPPs constructed by the ICP.

> 6 In the only precedent to this alternative approach I know of, Prados de la Escosura (2000) fills in the missing data on GDP at PPP both cross-sectionally and over time using a single model of the price level index, estimated on pooled data for OECD countries.

4
a host of direct transit and customs charges (for an exhaustive review of all transit trade costs faced by landlocked countries, see UNCTAD 2004). A comparison of these costs is shown in Appendix 3. Although the sample is too small to draw any general conclusions, the two Central Asia data points (Kazakhstan-Russian Federation and UzbekistanTurkmenistan) suggest that the costs are very high.

Moreover, border crossings also cause long delays to transit traffic (see Appendix 4). It is regularly observed that time delays and the variability of transit times are of greater concern to traders than direct costs, as they hinder the ability to meet delivery contracts without large inventory stocks (see Hummels, 2001).

Finally, according to the World Bank (2005), the most serious problem relates to customs and the incidence of unofficial payments, which is extraordinarily pernicious in Central Asia. This handicap compounds other customs-related impediments, such as the lack of coordination among border-related agencies, complexity of customs’ procedures, lack of transparency in customs’ codes and regulations, low utilization of information technology in customs’ operations, as well as long transit times.

# **_Limited choice of transit corridors_**

Finally, the limited choice of transit corridors does not allow Central Asian countries to negotiate with transit countries. Some new transit agreements are in development, but at the present time a number of transit countries continue to exercise a monopoly. As noted by UNESCAP 2003 **,** this restricted competition in Central Asia _in the provision of transit transport services_ between operators, modes of transport and routes may result in inefficient pricing policies and services.

# **3. ASSESSING THE IMPACT OF INFRASTRUCTURE AND LANDLOCKEDNESS ON CENTRAL ASIAN TRADE**

In this section we will address two important issues. First, to what extent is Central Asian trade affected by the level of internal infrastructure network? Second, how far is it reduced by higher transport costs due to landlockedness?

The gravity equation provides a general empirical framework and is especially suited to the examination of these issues. This model allows us to identify the impact on bilateral trade of variables, such as infrastructure and landlockedness, once all other structural determinants 12
In the surveys, two variables were used to measure cargo dwell time: (a) the average dwell time measured in days and (b) the distribution of dwell time, by length of time: 0–5 days, 6–10 days, 11–20 days, 21–40 days, 41–70 days, and more than 70 days.<sup>6</sup> The average cargo dwell time (measured in days) by country, weighted by the number of imported containers, is shown in Figure 10. South Africa has the shortest dwell time, as expected, close to what is identified as ―normal‖ in Europe or Asia. Nigeria has the longest. The average dwell time of the total five countries sample is around eight days.

These figures should be viewed with caution, because they are not necessarily representative and are less reliable than customs data. Data collected in firm surveys should only be used as a complement to customs data on dwell time.

**Figure 10: Cargo Dwell Times in Select African Countries, Weighted by the Number of** **<u>Imported Containers, 2011</u>**

<!-- Start of picture text --> 16<br>14.11<br>14<br>12<br>10 8.71<br>8.12<br>7.41 7.64<br>8<br>6<br>3.93<br>4<br>2<br>0<br>Kenya Nigeria South Africa Uganda Zambia Total<br><!-- End of picture text --> _Source:_ Authors based on firm surveys.

It is also interesting to analyze the distribution of dwell time by country (Figure 11). In South Africa 93 percent of imported containers have a dwell time between 0 and five days, which is expected, since the average is very low (3.93 days) compared to the other countries. In Kenya and Nigeria 69 percent and 74 percent respectively of their imports need between six and 20 days to be cleared; this is consistent with the average dwell time of around nine days for Kenya and fourteen days for Nigeria.

In Zambia, most of the imported containers (59 percent) have a dwell time between zero and five days; hence average dwell time is almost the same in Zambia (7.64 days) as in Uganda (7.41 days), even though only 16 percent of containers in Uganda have average dwell time of zero to five days, while 76 percent have average dwell time of six to eleven days. Zambian importers benefit from the relatively good performance of Durban port, which shows that tackling performance issues in a port has a positive spillover effect on landlocked countries.

> 6 This measures the proportion of containerized imports with a dwell time of 0–5 days, 6–10 days, 11–20 days, 21–40 days, 41–70 days, and more than 70 days.

22
ers. Furthermore, we assume that individuals consume all their labor income, such that cgam =lsamwsam-* Substituting this constraint into equation (9) and maximizing with respect to ls4jy leads to

# Isam = $sAMWgau

where the parameter @s4y, which equals ~~[Sepa~~ SAM varies across labor groups. As a result, the model allows us to capture differences in labor force participation rates across skill and age groups, and across natives and immigrants. These differences can be due to the heterogeneity in the disutility of labor, in the preferences for consumption, in cultural traits or in social norms. In addition, y = ~~i~~ is the elasticity of labor supply to income, which is assumed to be positive and common across labor groups.

The aggregate labor supply of workers of type SAM is obtained by multiplying (10) with the population size, Qsam, of that type:

# <u>dsam</u> = !samQsam = OsaMWgayQsam VS,A,M,

We can equivalently express this equation in vector format:

# 2.4 Labor market equilibrium

A competitive labor market equilibrium is defined as the following: Definition For a set of common elasticities {og,04,0m,7}, a set of group-specific parameters {5,0 sa,U sau}, and for a given composition of the labor force {Qsam}, the labor market equilibrium is a set of endogenous employment levels and wage rates {dsam;Gsam> Wsam} such that the optimal labor demand conditions (8) hold, the optimal labor supply conditions (12) hold, and the labor market clears in all groups, qsam = <u>dsam</u> VS, A, M.

Once the competitive equilibrium is computed, the model can be used to quantify the effect of exogenous labor supply shocks on employment levels and wage rates of each labor type.

There are four main sources of changes in the labor force: (1) changes in the number of immigrants in each age ~~-s~~ kill type as they enter/exit the country; (2) changes in the number of natives of each age ~~-s~~ kill type due to emigration; (3) changes in the number of

> 4 Allowing consumption to be a constant share of labor income does not alter the implications

10
# Table of Contents'

|A.|Background|1|
|---|---|---|
|B.|The Old Pension System|1|
|C.|The New Pension System|5|
|D.|Unique Features|13|
|E.|Future Challenges|21|
|F.|Concluding Remarks|24|

'This paper draws on work done for the Financial Markets and Pension Reform Project (World Bank 1995). I benefited from discussions with rnany colleagues at the World Bank as well as with Bolivian officials.
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13 areas. While the first result does not differ from that obtained using Kakwani's formulae, the second does, so that marginal proportional rates of substitution obtained using (11) would differ from Kakwani's. What is the net impact of growth on poverty? It can be found by using (10) or by estimating: LogPkt =g+LogWkt + k +lkt (12) The results are still given in Table 6 and they are illustrated in Figure 2, 4, and 6 for the headcount with the lower poverty lines. Nationally, when factoring in the impact of growth on inequality, a one percentage point increase in growth reduces the headcount index by 1.98 to 2.03 percentage points with the lower poverty lines, and 1.29 to 1.37 points with the upper poverty lines. The impact of growth on higher order poverty measures is similarly reduced as compared to what was obtained holding inequality constant. About one fourth of the potential gains from growth for poverty is lost due to higher inequality.

W.Whilth **e** results for the headcount index are similar in urban and rural areas, they differ for higher order poverty measures which are more sensitive to inequality. Consider the squared poverty gap with the upper poverty lines. The net elasticity of poverty to growth obtained with the fixed and random effects models in urban areas are -2.51 and -2.53, much below the gross elasticities at -3.53 and -3.52. In rural areas the net elasticities, at -3.50 and -3.59, are virtually equal to the gross elasticities, at -3.62 with the two models. This confirms that rural growth reduces inequality-sensitive poverty measures more than urban growth simply because growth is more associated with inequality in urban than in rural areas.

# _IV 3 Possibilitiesforfurther work_

The regressions used above remain descriptive in that we did not attempt to investigate the potentially complex relationships between past, current, and future growth, inequality, and poverty. Completing such an investigation would be beyond the scope of this paper, but a few potential topics can be highlighted in order to show the rich possibilities provided by analyses using regional panel data.
online appendix

# **A Data appendix**

## **A.1 EBM data**

We use the universe of EBM transactions made through EBM II as our original data from November 2019 through August 2020. EBM II is a software-based teller system introduced in 2018 as an alternative to traditional physical EBMs (EBM I); additional details on each are presented in Appendix A.1.1.<sup>A1</sup> We restrict to data in November 2019 and later, because the number of active EBM II devices in November increased by 50% relative to October, as part of a large EBM II registration drive.<sup>A2</sup>

**Coverage** EBM is mandated for use by VAT taxpayers, described in Appendix A.3. Receipts issued by registered taxpayers are transmitted via an internet connection to the RRA. In November 2019, EBM II recorded 81.8 billion RwF of value added, or 10.4% of GDP on 470,000 receipts.

**Receipt data** Each EBM is identified by a unique Sales Data Controller (SDC) ID, and issues receipts which are digitally signed with a unique Sales Data Controller Receipt Signature (SDCS). A fictitious EBM receipt is depicted in Appendix A.1.1. Each receipt enumerates the transacted items, and records the prices at (and quantities in) which they were sold.

EBM II receipts include both a United Nations Standard Products and Services A1 EBMs, or Electronic Billing Machines, are no longer exclusively physical machines. For this reason, Electronic Invoicing Systems (EIS) nomenclature is used as an umbrella term for both physical and digital/software-based receipt generating systems. To align with the colloquial usage in Rwanda, in this Appendix we refer to any official receipt generating system as EBM, and delineate versions when referring to any specific form of EBM (EBM I, EBM II). In the body of the paper, for parsimony we refer to EBM II as EBM, both because we do not use EBM I data for our analysis and also because of the relatively broad coverage of EBM II.

> A2Although EBM I offers more coverage before November 2019, these receipts are not comprehensively stored in a machine readable format.

A1
7 labor intensive products previously identified by the NBER as well as all other (capital. intensive) manufactured goods. For the initial tests the revealed comparative aevantage indices were computed for all developing countries as a group with all developed countries being the destination of exports. This procedure generated 119 distinct three, four and five-digit SITC labor intensive products and 88 similar capital intensive products which had matched RCA and labor intensity indices. Since there was an interest in determining how the relation between the two comparative advantage indices might vary between developing countries at different levels of industrialization, revealed comparative advantage indices were also computed for the 207 labor and capital intensive goods exported from two selected groups of developing countries: Asian semi-industrialized exporters of manufactures (Hong Kong, Republic of Korea, Singapore and Taiwan, China) and a group of "other" South Asian developing countries (Indonesia, Malaysia, Thailand and the Philippines).

Table 1 provides summary statistics on comparisons between the Heckscher-Ohlin indices and matched developing countries' revealed comparative advantage indices for both the NBER labor intensive and other capital intensive products. To determine how the relationship may have changed these comparisons are made for different years; 1965, 1975 and 1985. In addition, the value of imports from developing countries is shown for these three years.' To assist 7 Erzan and Yeats (1989) examined broad two-digit U.S. Standard Industrial Classification (SIC) changes in factor intensities over this interval and generally f(und that sectors using relatively labor-intensive techniques in the earlier period employed similar techniques in 1982. Petroleum and coal products (SIC 29) and tobacco products (SIC 21) were exceptions as both sectors experienced a major increase in capital intensity while primary metals (SIC 33) became more labor intensive. Erzan-Yeats also found that textiles and clothing became slightly more labor intensive in spite of high levels of protection under the MFA which was intended to provide the industry with opportunities to implement structural adjustment measures.
checked test-retest one week apart, had a kappa (measure of agreement) of 0.62 (McCoy et al. 2018).

Inter-rater reliability is also an issue; even if they were observing the _exact_ same phenomena, enumerators might provide different measures and responses (Gwet 2014). Enumerators, during training, were required to achieve scores of at least 80% on activities and quizzes in an effort to support inter-rater reliability. As a point of reference, the _Teach_ classroom observation tool, designed for LMICs, in a validation study where there were two enumerators engaging in identical observations in the classroom, had an ICC of 0.75. Across categories exact agreement on items between raters ranged from 54% to 79% (Molina et al. 2018).

We focus on an outcome of consistency, since our data collection setup does not allow us to distinguish these different drivers of measurement error. This focus also better reflects how QAS are actually implemented: with different raters and at different times. We look at consistency both for individual monitoring tool items and for different dimensions and subdimensions of the QAS.

In terms of individual items, the QAS tool includes: _Basic classroom and teacher information:_

- Class size

- Number of teachers

- Primary teacher highest education level

- Primary teacher specialization

- Primary teacher year started teaching

- In-service training in the past 12 months

- Topics of in-service training

# _Materials_

- Writing utensils (none present; present but children do not use; present some children use; present all children use)

- Manipulatives (none present; present but children do not use; present some children use; present all children use)

- Classroom management tools (none present; present but teachers do not use; present teachers use)

# _Pedagogy (yes/no)_

- Teacher engages in individual instruction

- Teachers and children have back-and-forth discussion/dialogue

- Teacher asks open-ended questions

- Teachers use strategies of Egypt’s education 2.0 curriculum

- Teacher connects lessons to real life

- Teacher is mostly positive (warm, responsive)

- Teacher redirects misbehavior

11
Figure A2: Percentage of green ECTs and firms by NAICS sector, 2012-2021 A. Distribution of ECTs mentioning at least one green technology across sectors <!-- Start of picture text --> 100<br>80<br>60<br>40<br>20<br>0<br>Information Manufacturing Professional Services Utilities Other Sectors<br>B. ECTs mentioning at least one green technology within each sector<br>30<br>Manufacturing<br>Information<br>Professional, Scientific, and Technical Services<br>Utilities<br>Other Sectors<br>20<br>10<br>0<br>2012 2013 2014 2015 2016 2017 2018 2019 2020 2021<br>2012 2013 2014 2015 2016 2017 2018 2019 2020 2021<br>% of green ECs by NAICS sector<br>% of ECTs mentioning green tech<br><!-- End of picture text --> Notes: Panel A depicts the proportion of ECTs that mention green technologies by NAICS sector over the period 2012-2021. Panel B depicts the percentage of ECTs that are green within each NAICS sector over the period 2012-2021.

34
Policy Research Working Paper 8363

# **Abstract**

This paper studies the prevalence and consequences of size-dependent tax enforcement and compliance. The identification strategy uses the ranking of industries’ average firm size in the United States as an instrument for the size ranking of the same industries in developing countries. Data on 125,000 firms in 140 countries show that tax enforcement and compliance increase with size. Size-dependence is more prevalent in low-income countries, and concentrated at the top of the size distribution. When quantified in a general equilibrium model, removing size dependent enforcement leads to gains in Total Factor Productivity of up to 0.8 percent.

This paper is a product of the Macroeconomics and Growth 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 pbachas@worldbank.org and rfattaljaef@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
# **Bribery, Plant Size and Size Dependent Distortions**<sup>*</sup>

## **M. Nazım Tamkoç**<sup>†</sup> _World Bank_

For the latest version, please click here

**Keywords** : Bribery, Misallocation, Distortions, Plant Size, Development, Corruption **JEL Classification:** E23, L25, O41, O49

> *I would like to thank Gustavo Ventura for his guidance, support, and contributions. I am also grateful to Juhee Bae, Roberto Fattal Jaef, Domenico Ferraro, David Francis, Berthold Herrendorf, Norman Loayza, David McKenzie, Bob Rijkers , Jorge Luis Rodríguez Meza, Yunus Topbas, Jesica Torres and all the participants in ASU Macro Workshop, Midwest Macroeconomics Group Meeting in Vanderbilt University and World Bank DECIG Half-baked Seminar for their useful comments and suggestions. The findings, interpretations, and conclusions expressed in this paper are entirely those of the authors. They do not necessarily represent the views of the World Bank and its affiliated organizations, or those of the Executive Directors of the World Bank or the governments they represent.

- †The World Bank **E-Mail** : mtamkoc@worldbank.org