rafmacalaba/datause-encoder-data huggingface dataset:
headcount assumes that the rate of poverty in the Democratic People’s Republic of Korea is the average poverty rate in East Asia. The systematic correlation between missing survey data and national income observed in Figure 1 suggests that the assumption of missing at random is untenable, and can result in a downward bias in the global poverty count.

While the availability of household consumption surveys is essential for tracking poverty, the quality of these surveys is no less important. In particular, where household consumption data are available, it should not be taken for granted that these data are comparable over time. For example, Serajuddin _et al._ (2015) find that over the period 2002- 2011, almost one-fifth (i.e., 28) of the 155 countries for which the World Bank monitors poverty data using the WDI database have only one poverty data point.<sup>3</sup> As another example, a recent survey by Beegle _et al._ (2016) points out that just more than half (i.e., 27) of the 48 countries in Sub-Saharan Africa had two or more comparable household surveys for the period between 1990 and 2012. Even countries with a respectable and long-running household survey such as India from time to time run into issues with survey comparability (Deaton and Kozel, 2005; Dang and Lanjouw, forthcoming).<sup>4</sup> Perhaps this lack of survey comparability may be ameliorated if (better) data can be produced more frequently.

A less rosy picture emerges with panel survey data, where far fewer developing countries can afford to collect such data. Tracking the same household (or individual) over time is a costly undertaking for various reasons. For example, households can grow larger with new members (through birth or marriage) or smaller (with previous members dying or migrating) over time, or the whole household can migrate, making it more difficult for interviewers to follow them.

> 3 In addition, Serajuddin et al. (2015) also find that as many as 29 countries do not have any poverty data point in the same period.

> 4 Household consumption data may not exactly be comparable across different countries without a common conversion system for prices of goods and services. The International Comparison Program (ICP) is an international effort to address this issue (World Bank, 2015). See also Crossley and Winter (2015) and the 2015 special issue of the _Journal of Development Studies_ for further discussion on survey comparability issues.

4
services, suggesting that the barrier to use transit is higher in Kinshasa. The average accessibility curves ascend from the inflection point onward, however, not at the same rate. Increasing travel budget thresholds result in smaller increases in average accessibility in Kinshasa than in Rio, and above the 80 minutes threshold Kinshasa’s curves begin to plateau with increasing travel budgets. In Rio there are consistent increases in accessibility with both increasing affordability and travel time.

In summary, we can extract three main messages from Figure 4. First, travel time thresholds are the main driver of accessibility in Kinshasa, as can be seen in Figure 4d. When households accept to travel for 120 minutes, average accessibility in Kinshasa is only multiplied by approximately 3 when they spend 100% of their income on commuting instead of 0% (a little over 60% vs 20%, respectively). This is a testimony to the power of compactness and density in enabling connections between households and job opportunities. Second, both travel time and travel budget thresholds are important for average accessibility in Rio, but travel budgets have a much stronger role in Rio than in Kinshasa as can be seen by the much larger spread of accessibility values in Rio for the 120-minute time threshold than in Kinshasa (Figures 4a and 4c). This is indicative that transit and its affordability play a large role in compensating for Rio’s fragmented spatial layout (relative to Kinshasa). Third, transit is much more affordable in Rio than in Kinshasa on average. Figures 4b and 4d indeed show that accessibility starts increasing when households can spend about 5% of their income on transport in Rio, whereas this only manifests itself around the 10% mark in Kinshasa.

In cases where comparisons of the two cities will be undertaken, a travel time threshold of 75-minutes will be used. This threshold provides a cross-section where residents are able to use transit services for mobility, there is clear variation between the travel budget thresholds and no secondary inflection point with respect to travel time and travel budget threshold increases has been reached, which could potentially convolute results.

Figure 5 shows the accessibility spatial distribution in Rio de Janeiro and Kinshasa, with increasing travel budget thresholds. This analysis is further supported by baseline accessibility distributions per income decile, which are shown in Appendix C – Baseline Accessibility per Income Decile. At low travel budget limits, accessibility is concentrated around the economic centers (Centro and Gombe) of both cities. However, as the limits get higher, accessibility levels increase along key transit corridors away from the economic hubs. This is particularly concentrated along rail, metro and BRT corridors in Rio and along the N1 between Bon-Marche and Matete in Kinshsasa. Residents located at the peripheries of both cities generally have very low accessibility to economic opportunities.

14
Self-Employment in the Developing World

# T. H. Gindling<sup>*</sup> David Newhouse<sup>†</sup>

# Keywords: Self-Employment, informality, entrepreneurship, development.

JEL codes: J21, O17

* Department of Economics and Public Policy, University of Maryland Baltimore Country, and Institute for the Study of Labor, Bonn, Germany.

† Social Protection and Labor Team, World Bank, Washington DC, and Institute for the Study of Labor, Bonn, Germany. This paper is a background paper for the World Bank’s 2013 World Development Report on Jobs. The study was funded by the governments of Austria, Germany, and Norway, South Korea, and Switzerland under the auspices of the Multi Donor Trust Fund on Labor Markets, Job Creation, and Economic Growth. We thank David Margolis, Arup Banerji, and Kathleen Beegle for useful substantive discussions and suggestions. We also thank Arup Banerji, David Robalino and Martin Rama for support, Bill Maloney for helpful comments, and Claudio Montenegro and his team for compiling and providing the data.

1
Policy Research Working Paper 7232

# **Abstract**

Many production firms use intermediary trading firms to export indirectly. This paper uses Chinese export data at the transaction level to investigate the tax evasion motive through indirect trade. The paper provides strong evidence that, under China’s partial export value-added tax rebate policy, production firms can effectively evade value-added taxes by underreporting their selling prices to domestic intermediary trading firms, especially when they sell differentiated products. Even for a moderate level of underreporting, the revenue loss is close to one billion U.S. dollars. The paper also finds that such underreporting behavior through domestic intermediaries may be associated with cross-border evasion through underreporting export values to foreign partners. In addition, the results indicate that the evasion motive is stronger for larger transactions.

This paper is a product of the Trade and Competitiveness Global Practice 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 mferrantino@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
distinguish the effects of nondiscriminatory regulation from discriminatory policies. Regulations generally will increase fixed and/or variable costs of production for firms, and may result in a _de facto_ or _de jure_ exclusion of new entry, thereby increasing prices. Insofar as regulation is motivated by market failures created by the characteristics of specific service industries – e.g., network externalities, asymmetric information – such price impacts may be social welfare enhancing.

Much of the existing research mapping policies into “restrictiveness indices” has been done by and for the Australian Productivity Commission. This work suggests that barriers to services trade appear to be substantial, especially for modes 3 and 4, in the sense that there is significant discrimination against foreign providers of services (Findlay and Warren 2000). Some of the results of this sector-specific research are discussed further below; a number of the price and costs estimates of the impacts of the restrictiveness indices that were are reported in Table 2.

**Table 2. Some estimates of price/cost impacts of services policies**

||||**OECD**<br>|**countr**|**ies**<br>|**Developi**<br>|**ng cou**|**ntries**<br>|
|---|---|---|---|---|---|---|---|---|
|**Sector**|**Source and**<br>**period covered**|**Measure**|**Simple**<br>**average**|**σ**|**N**|**Simple**<br>**average **|**σ**|**N**|
|Maritime<br>shipping|Clark, Dollar and<br>Micco (2001);<br>2000|Percentage impact on shipping<br>costs of mandatory use of<br>certainport services|2.0|2.6|21|5.6|3.5|32|
|Air transport :<br>economy fare|Doove et al.<br>(2001); late 1990s|Estimated increase (%) in fares<br>over an estimated “free trade”<br>level for a set of bilateral<br>routes|30.6|19.5|23|63.9|19.6|12|
|Air transport :<br>APEX<br>discount fare|Doove et al.<br>(2001); late 1990s|Estimated increase (%) in fares<br>over an estimated “free trade”<br>level for a set of bilateral<br>routes|8.9|4.4|23|16.8|3.5|12|
|Retail food<br>distribution|Kalirajan (2000);|Impact on costs of barriers on<br>foreign establishment|2.7|1.7|12|2.3|3.2|6|
|Retail<br>banking|Kalirajan et al.<br>(2000); 1996-97|Percentage impact on net<br>interest margins of<br>discriminatory policies|11.8|11.6|7 (a)|31.8|19.0|9|
|Engineering|Nguyen-Hong<br>(2000);1996|Impact of barriers to FDI on<br>price cost margins(%)|5.2|4.1|14|8.4|4.3|6|
|Mobile<br>telecom|Doove et al<br>(2001); 1997|Price impact (%) of regulatory<br>policies relative to a notional<br>benchmark regime|26|27|24|21|15|18|
|International<br>telecom|Doove et al<br>(2001); 1997|Price impact (%) of regulatory<br>policies relative to a notional<br>benchmark regime|73|61|24|34|9|18|

_Notes_ : (a) Includes the EU-15 as one observation. _Source_ : Data compiled in part from tables reported in Deardorff and Stern (2006) and Dee (2005). The Doove et al. (2001) study draws extensively on the results of Boylaud and Nicoletti (2000) and Gönenç and Nicoletti (2000).

19
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# **PUBLIC TRANSPORT SUBSIDIES AND AFFORDABILITY IN MUMBAI, INDIA**

## **I. Introduction**

This paper describes the role of public transport and the nature and incidence of transport subsidies in Mumbai, India. Mumbai has an extensive rail and bus system, and public transport is used for over 75% of all motorized trips in Greater Mumbai. Both rail and bus fares in Mumbai are subsidized: BEST, which operates public buses in Mumbai, also is also an electric utility, and subsidizes bus fares from electricity revenues. The Central and Western railways (part of Indian Railways) operate rail services in suburban Mumbai. Although rail fares cover operating costs, they do not fully cover capital costs; hence there is an implicit supply-side subsidy to rail fares in Mumbai.

We analyze the incidence of these subsidies, and their effect on mode choice, using data from a survey of households in Greater Mumbai conducted in the winter of 2003-2004 (Baker et al., 2005). In Mumbai, as in many other cities, the middle class is more likely to use public transport for travel than the poor. The poor, however, also use public transit, and their expenditure on public transit constitutes, on average, a larger share of their income than it does for the middle class. It is, therefore, the case that the poor benefit from transit subsidies in Mumbai, as well as the middle and upper-middle classes; however, the poorest 27% of the population receives only 19% of bus subsidies and 15.5% of rail subsidies.<sup>1</sup> Indeed, 26% of the lowest income households surveyed do not use rail, while 10% do not use bus, implying that they receive no transit subsidies.

The remainder of the paper is organized as follows. The second section presents an overview of Greater Mumbai and its rail and bus services. Section 3 describes the travel patterns of households in Greater Mumbai. In section 4 we present information on household expenditure on transport and discuss the structure of rail and bus fares. Section 5 discusses the magnitude of transit subsidies and section 6 their incidence and the impact on mode choice. Section VII concludes.

## **II. Background**

Greater Mumbai, the focus of this study, constitutes the core of the Mumbai Metropolitan Region (MMR). Greater Mumbai, with a population of 11.9 million people in 2001, occupies 468 sq. km. This makes Mumbai one of the most densely populated cities in the world. During the decade 1991-2001 the population of Greater Mumbai grew at a rate of approximately 1.8 percent annually—less than the national average. This reflects a declining rate of migration into the city and the more rapid growth of the Mumbai Metropolitan Region (MMR), one of the world’s largest with a population in 2001 of 18 million. The city faces enormous challenges with shortages of land, housing,

> 1 In Baker et al. (2005) the lowest income group studied consists of households earning less than Rs. 5,000 per month.  They constitute 26.6% of the sample of 5,000 households surveyed.

2
# Table D2. Distribution of Manufacturing Establishments by Source of Electricity and Employment Size, Thailand

<!-- Start of picture text --> ELECTRIC (SOURCE OF ELECTRICITY) FIRMSIZE (EMPLOYEES per FIRM)<br><!-- End of picture text --> <!-- Start of picture text --> Frequwncy<br>Percent ;<br>Rai Pct<br>Col Pet 11-19 120-49 150-99 1100-199 1200-499 1500-999 11000 & I Total<br>... I I t I I lover I<br>............----- ........ + .......................--- ....... ........ ..........<br>Pubilc onLy 33 721 671 53 .39 131 5 282<br>11.00 24.00 1 22.33 17.67 1 13.00 4.33 1 1.67 94.00<br>t11.70 25.53 t23.76 t18-. t 13.83 t 461 i l n<br>100.00 97.30 98.53 96.15 82.98 81.25 62750<br>............ ........ ........ ... .. - ........<br>PubtiUc _min 0 1 1 1 7 3 3 16<br>0.00 0.33' 0.33 0.33 2.33 1.00 1.00 5.33<br>0.00 6:25 6.25 6.25 U .75 118.75<br>. 0.001 1.35t 1.471 1.85 I 14.891 18.75 37.501<br>.....................<br>own O I O O I 1 0 2<br>Generators 0.00 0.33 0.00 0.00 0.33 0.00 0.00 0.67<br>_in 0.00 50.00 0.00 0.00 1 50.00 0.00 1 0.00<br>0.00 1.35 0.00 0.001 2.131 0.00 0.00<br>........... ........<br>Total 33 74 68 54 47 16 8 300<br>11.00 24.67 22.67 18.00 15.67 5.33 2.67 100.00<br><!-- End of picture text -->
PPR Working Paper Series

|||IBL<br>Au|X|Contact<br>forpaer|
|---|---|---|---|---|
|WPS294|Irreversibility, Uncertainty, and<br>Investment|Robert S. Pindyck|October 1989|N. Carolan<br>61737|
|WPS295|Developing Country Experience<br>in Trade Reform|Vinod Thomas|October 1989|S. Fallon<br>61680|
|WPS296|How Serioub ;s the Neglect of<br>Intrahousehold Inequality?|Lawrence Haddad<br>Ravi Kanbur|November 1989|J. Sweeney<br>31021|
|WPS297|Effects of the Multi-Fibre<br>Arrangement on Developing<br>Countries' Trade: An Empirical<br>Investigation|Refik Erzan<br>Junichi Goto<br>Paula Holmes|November 1989|L Tan<br>33702|
|WPS298|Evaluating Global Macroeconomic<br>Models: A Case Study of<br>MULTIMOD|<br>Ahmad Jamshidi|December 1989|M. Divino<br>33739|
|WPS299|The External Effects of Public<br>Sector Deficits|Carlos ANfredo<br>Rodriguez|November 1989|R. Luz<br>61588|
|WPS300|How the 1981-83 Chilean Banking<br>Crisis was Handled|Mauricio Larrain|December 1989|WDR Office<br>31393|
|WPS301|Myths of the West: Lessons from<br>Developed Countries for Developm<br>Finance|Collin Mayer<br>ent|November 1989|WDR Office<br>31393|
|WPS302|Improving Support Services for<br>Rural Schools: A Management<br>Perspective|Sherry Keith|November 1989|J. Cheeseman<br>61703|
|WPS303|lI Undernutrition Responsive to<br>Changes in Incomes?|Martin Ravallion|November 1989|C. Spooner<br>30464|
|WPS304|The New Political Economy:<br>Positive Economics and Negative<br>Politics|Merilee S. Grindle|||
|WPS305|World Bank Work with<br>Non-Governmental<br>Organizations|Lawrence F. Salmen<br>A Paige Eaves|December 1989|E. Madrona<br>61712|
|WPS306|A Method for Macroeconomic<br>Consistency in Current and<br>Constant Prices|Ali Khadr<br>Klaus Schmidt-Hebbel|December 1989|S. Jonnakuty<br>61769|
|WPS307|On the Accuracy of Economic<br>Observations: Do Sub-Saharan<br>Trade Statistics Mean Anything|Alexander J. Yeats|November 1989|J. Epps<br>33710|
Dix-Carneiro and Kovak Trade Reform and Regional Dynamics Table 4: Evolution of Formal Earnings and Employment by RTC Quartile

|Regional Tariff Change quartile:|1<br>most nega|2<br>tive|3<br>mo|4<br>stpositive|
|---|---|---|---|---|
|1991 log real formal earnings level|5.79|5.60|5.53|5.44|
|change in log real formal earnings|||||
|1991-1995|0.25|0.27|0.25|0.25|
|1991-2000|0.24|0.25|0.27|0.29|
|1991-2005|0.23|0.29|0.34|0.38|
|1991-2010|0.49|0.59|0.64|0.68|
|change in log formal employment|||||
|1991-1995|0.05|0.09|0.11|0.11|
|1991-2000|0.10|0.16|0.20|0.23|
|1991-2005|0.19|0.30|0.34|0.37|
|1991-2010|0.32|0.43|0.47|0.49|

Formal earnings and employment from RAIS data. Regional Tariff Changes calculated based on equation 3. See text for details. 475 microregion observations. Earnings deflated using the national consumer price index. Regional earnings calculated as average regional residuals after controlling for regional worker composition with demographic characteristics and industry fixed effects.

66
<u>Table A.2: Correlations of Assessment Results</u>

|_Panel A: Correlation_|_s In First 17 Days _|_of Asses_|_sment (1,615_|_workseekers)_|
|---|---|---|---|---|
||Concept formation|Grit|Numeracy|Control<br>Flexibility<br>(Personal<br>(Approach<br>Control)<br>Avoidance)|
|Communication|0.337|0.127|0.386|0.237<br>0.126|
|Concept formation||0.108|0.489|0.174<br>0.098|
|Grit|||0.163|0.507<br>0.334|
|Numeracy||||0.212<br>0.107|
|Control||||0.173|
|_Panel B: Correlation_|_s In Remaining 67 _|_Days of _|_Assessment _|_(5,276 workseekers)_|
||Concept formation|Grit|Numeracy|Focus<br>Planning<br>(Stroop)<br>(Hit 15)|
|Communication|0.346|0.088|0.393|0.171<br>0.258|
|Concept formation||0.094|0.519|0.225<br>0.292|
|Grit|||0.129|0.049<br>0.106|
|Numeracy||||0.162<br>0.325|
|Focus||||0.181|

Table shows pairwise correlation coefficients between assessment results. The sample is split because two of the assessments changed after the first 17 days of assessment, from the control and flexibility scales to the focus and planning tasks. None of the pairwise correlations between the four assessments used for the entire period (communication, concept formation, grit, and numeracy) are substantively or statistically significantly different between the two periods.

Table A.3: Distribution of Top, Middle, and Bottom Terciles Shown on Candidates’ Reports

||||0|Fr<br>1|action w<br>2|ith _ t<br>3|op terci<br>4|les<br>5|6|Total|
|---|---|---|---|---|---|---|---|---|---|---|
|||0|0.001|0.007|0.025|0.032|0.029|0.018|0.007|0.119|
|h _|iles|1|0.009|0.036|0.059|0.064|0.037|0.011|-|0.215|
|wit|erc|2|0.027|0.077|0.079|0.040|0.011|-|-|0.235|
|n|m t|3|0.054|0.076|0.048|0.009|-|-|-|0.187|
|tio|to|4|0.070|0.059|0.009|-|-|-|-|0.138|
|rac|bot|5|0.060|0.024|-|-|-|-|-|0.084|
|F||6|0.023|-|-|-|-|-|-|0.023|
|T|ota|l|0.243|0.279|0.220|0.146|0.076|0.029|0.007||

Table shows the share of the sample with _i_ top terciles and _j_ top terciles on their reports for each _i, j ∈{_ 0 _,_ 6 _}_ . The number of middle terciles equals 6 _− i − j_ .

41
# **Figure 4A: CDF of Monthly Profits of Current Enterprises at First follow-up Survey**

<!-- Start of picture text --> 0 20000 40000 60000 80000 100000<br>Real Monthly Profits in Round 2<br>Control Cash<br>Training only<br>1<br>.8<br>.6<br>.4<br>.2<br>0<br><!-- End of picture text -->

**Figure 4B: CDF of Monthly Profits of Current Enterprises at Last follow-up Survey**

<!-- Start of picture text --> 0 20000 40000 60000 80000<br>Real Monthly Profits in Round 5<br>Control Cash<br>Training only<br>1<br>.8<br>.6<br>.4<br>.2<br>0<br><!-- End of picture text --> 32