rafmacalaba/datause-encoder-data huggingface dataset:
|Annual Revenue|208|259 (24)|242 (16)|1 (-99)|
|---|---|---|---|---|
|Annual Cost|271|375 (38)|268 (-1)|271 (-0.04)|
||**Taxi (MA**|**D)**|||
|Annual Profit|0|0|0|0|
|Annual Revenue|1,418|3008 (112)|1,712 (21)|1,407 (-0.74)|
|Annual Cost|1,418|3008 (112)|1,712 (21)|1,407 (-0.74)|
|Fare(per trip)|7.09|12.6 (78)|7.01(-1)|7.08(-0.05)|
|Total Taxi|15000|17904 (19)|18,322(22)|14,897(-0.7)|
||,<br>**Tram (M**|,<br>**AD)**|||
|Annual Profit|-227|-217 (5)|-222 (2)|-228 (-0.24)|
|Annual Revenue|57|67 (18)|62 (10)|56 (-1)|
|Annual Cost|284|284 (0)|284 (0)|284 (0)|

Clearly the existing levels of the fuel or parking tax are too low relative to the optimal levels and huge hikes are necessary to achieve efficiency. This is an important result especially because under both of these policies consumer expected utility decreases by 0.48% and 0.26%. This is because the utility gain from congestion alleviation is offset by the increased disutility from bus crowding and more importantly by the income effect of the large fuel or parking tax hikes. These results have strong policy implications. First, increasing the fuel tax and the parking tax by 10 times to reduce congestion would be a tough and unpopular decision for the government because it invites a strong opposition from the general public even though it helps reduce congestion and improves social welfare. Second, demand side instruments alone may not be effective enough if adequate infrastructure is not added to facilitate the switching from private to public transportation. On the other hand, demand side instruments have some desirable distribution implications. For example, not only does the population that relies on public transportation benefit from the congestion reduction, but the revenue collected from the increased fuel or parking taxes could be expended to improve education, health or public transportation. This would be a popular revenue transfer mechanism from the rich, who can afford private automobiles and would be the ones paying the higher fuel and parking taxes, to the poor who rely more heavily on education and health services and on better public transportation.

In order to encourage the use of high occupancy vehicles such as buses, it is optimal that the bus fare be reduced to zero. On the one hand, the lowering of the bus fare improves the expected utility, but on the other hand, the expected utility suffers due to the increased crowding in buses. Overall, there is a small increase in expected utility. But the social welfare increase is only 0.04% 21
# Table C4. Distribution of Manufacturing Establishments Boreholes by Employment Size, Indonesia

<!-- Start of picture text --> OREMOLE (OWN BOREOLES FOR PRODUCTION) FIRMSIZE (EMPLOYEES per FIRM)<br><!-- End of picture text --> <!-- Start of picture text --> Frequ ncy<br>Percent<br>NOW Pct<br>Col Pct 11-19 120-49 150-99 1100-199 1200-499 500-999 11000 a I Total<br>I I I I II lover I<br>.............. +.............. _+++. . ..... ..... ... ,,,,...............<br>YES 1' U 81 1 32 1443 129 13' 81 174<br>0.34 I 16.55 11.03 14.83 a I 10.00 I 4.48 1 2.76 60.00<br>0 57 27.59 1 18.39 ; 24.711 16.671 7.471 4.601<br>20.00 |51.06 I 57.14 1 69.35 1 65.91 t 68.42 1 80.00<br>._. ++.. .. ....... ........... ... ........ .............. . .. ....... ....... .<br>Ho 1 31 43 22 191 15i 61 21 110<br>1.03 14.83 7.59 6.55 5.17 2.071 0.69 37.93<br>2:73 39.09 20.00 17.27 13.64 ¶ 1 5.45 1.82<br>1 60.00 1 45.74 1 39.29 1 30.65 1 34.09 31.58 20.00<br>. .. .. .. .. .. . . .. . .. . . -- -- - -- - - - - - -- - - - - - -------- --------<br>N/A 11 31 2 1 01 0 O : 01 6<br>0.34 1 1.03 1 0.69 1 0.00.1 0.00 I 0.00 I 0.00 1 2.07<br>16.67 1 50.00' 33.33' 0 00 ° 0i 0.001 0.00 1<br>20.00 1 3.19 3:57 0.00 , 0.00 0.00 I 0.00 I<br>Total 5 94 56 62 44 19 10 290<br>1.72 32.41 19.31 21.38 15.17 6.55 3.45 100.00<br><!-- End of picture text -->
**Vietnam’s Labor Market Institutions, Regulations, and Interventions: Helping People Grasp Work Opportunities in a Risky World**

Achim D. Schmillen and Truman G. Packard The World Bank; 1818 H St., NW; Washington, DC 20433; USA; phone: +1 (202) 473 1000; email: aschmillen@worldbank.org and tpackard@worldbank.org _JEL_ -Classification: J08, J58, O15 Keywords: labor market policies, equality of opportunity, Vietnam Acknowledgements: We thank Victoria Kwakwa and Jehan Arulpragasam for guidance and Wendy Cunningham, Gabriel Demombynes, Sandeep Mahajan, Philipp O’Keefe and Sudhir Shetty for helpful comments and suggestions. María Antonia Remenyi provided valuable inputs on employment protection legislation in Vietnam. This paper draws on work conducted for the “Vietnam 2035 Report”, a forthcoming joint publication of the Government of Vietnam and the World Bank. Findings, interpretations and conclusions expressed in this paper are entirely our own and do not necessarily represent the views of the World Bank, its affiliated organizations, its Executive Directors or the governments these represent.
Global Investment Costs for Coastal Defense through the 21st Century Report to the World Bank 46

|**Source**|**Analysis**|**Country**|**Spatial scale or**<br>**location**|
|---|---|---|---|
|Dronkers et al.<br>1990|Global adaptation analysis.<br>Unit costs are based on<br>measures as applied in The<br>Netherlands and assumed<br>standard dimensions.<br>Country cost factors are<br>derived from expert<br>judgment and applied to<br>other countries|Any|Country level<br>estimates|
|Hoozemans et<br>al. 1993|Global vulnerability<br>assessment. As in Dronkers<br>et al. (1990), improved by<br>continuous cost functions of<br>dikes and dunes, and<br>inclusion of extreme sea<br>levels|Any|Country level<br>estimates|
|Environment<br>Agency 2008|Technical feasibility and cost<br>analysis for upgrading<br>protection standards of fixed<br>defenses in the Thames<br>Estuary|United Kingdom|Thames Estuary|
|Linham, Green,<br>and Nicholls<br>2010|Adaptation cost analysis of<br>the world’s largest port cities|Australia,<br>France,<br>Germany, Italy,<br>New Zealand,<br>South Africa,<br>Spain, United<br>Kingdom, United<br>States -<br>California,<br>Mozambique|Country or state level<br>estimates|
|Jonkman et al.<br>2013|Case study comparison.<br>Collection of project-based<br>information and conversion<br>in comparable units|The Netherlands|Country level<br>estimates for urban<br>and rural areas|
|||United States|New Orleans|
|||Vietnam|Hai Phong / Nam<br>Dinh|
|Lenk et al. 2016|Regression analysis.<br>Comparison with results of<br>other meta studies|Netherlands|Country level<br>estimates|
|||||satisfaction with the<br>training.||
|---|---|---|---|---|---|
|**Comprehension**<br>**test**|July 16<sup>th</sup>|All<br>treatment<br>schools<br>(3,000)|Online|Understanding of the<br>content in the Guide<br>captured by 12 multiple<br>choice questions.|45%|
|**First-follow up**|August<br>25<sup>th</sup>|All<br>schools<br>(4,000)|Online|Perceptions on school<br>dropout, returns to<br>education and influence<br>on dropout decision, as<br>well as tasks prioritized<br>by principals.|34%|
|**Second follow-**<br>**up-follow**|September<br>25<sup>th</sup>|All<br>schools<br>(4,000)|Online|Guide and list take-up,<br>implementation details<br>and degree of targeted<br>support.|24%|
|**Third follow-up**|April 15<sup>th</sup>-<br>May15<sup>th</sup><br>(next year)|All<br>schools<br>(4,000)|Paper-<br>based|Perceptions on school<br>dropout, returns to<br>education and influence<br>on dropout decision, as<br>well as targeted support<br>and tasks prioritized by<br>principals + Guide and<br>list take-up and<br>implementation details.|85%|

Below, we summarize the main data sources for different parts of the analysis, including program participation, outcomes, and potential mechanisms.

# **Program Participation**

To assess the take-up of the program we utilize multiple data sources. For treatments 1 and 2, we use school principals’ self-reports on the reception of the dropout prevention guide (which is also 16
disaggregation of export products according to the Standard Industrial Classification review 3 (SIC-3) of 4 digits for the period 1993-2009.<sup>14</sup> Starting with the simpler measurements of diversification, the more than 1,228 categories of export products are first ranked independently for each year covered in the sample. Once ranked, the value of exports of the major categories are added up and calculated as a percentage of total exports in the year. Figure 7 shows the evolution of the proportion of the value of total exports of the main 5, 10, 15 and 20 products, using the data by customs tariff sections.<sup>15</sup> The different versions of the indicator yield a similar trend, i.e. a slight trend to diversification (downward in the Figure) between 1993 and 1998 (except for the case of the principal 5 products in which the trend seems less evident) followed by a phase of secular growth of the concentration (interrupted by slight declines in some years of the sample). The 5 principal categories account for nearly 47% of exports in 1993, 45% in 1999 (at its nadir), and from then onwards a gradual upward leads to 59% in 2009. In the other versions of this indicator shown in Figure 6, the results are similar, with a significant increase of around 10% in the share of the leading products between 1999 and 2009.

**Figure 7: Proportion of Total Export Value Principal Exports by Customs Tariff Sections, 1993-2009**

<!-- Start of picture text --> 85%<br>80%<br>75%<br>70%<br>65%<br>60%<br>55%<br>50%<br>45%<br>40%<br>1993 1995 1997 1999 2001 2003 2005 2007 2009<br>First 5 First 10 First 15 First 20<br><!-- End of picture text --> Similarly, the standardized Herfindahl-Hirschman index yields a generally upward evolution, indicating declining diversification.<sup>16</sup> Figure 8 suggests an association between the evolution of the index and that concentration levels over time, where the number of products is not constant, it is necessary to standardize the index so it would be within the range of 0 to 1. Other indices used in the literature to measure the degree of export diversification include the Concentration Indices (CI), the Hall-Tideman Index (HTI), the Rosenbluth Index (RI), the Comprehensive Concentration Index (CCI), the Hannah-Kay Index (HKI), the Entropy Measure (EM) and the Diversification Index (DI). Bailey and Boyle (1971) provide a review of alternative indices and confirm that most yield comparable results.

14 A useful exercise to disaggregate the impact of prices from volumes in changes in diversification trends would have used trade date in constant values. However, this could not be attempted in this study as there are no deflators currently available in Peru by 4-digit SIC sectors.

15 The sections are aggregations of 4-digit tariff subheadings.

16 A similar exercise using the Hide-Tideman Index yields broadly similar results to those of the HerfindahlHirschman index, and is thus not reported in the body of the paper.

13
# **List of Tables**

|1|Probit Regressions - Complete Sample<br>. . . . . . . . . . . . . . . . . . .|13|
|---|---|---|
|2|Probit Regressions - by Country . . . . . . . . . . . . . . . . . . . . . . .|15|
|3|Type of Business Activity<br>. . . . . . . . . . . . . . . . . . . . . . . . . .|16|
|4|Multinomial Logit Model - Ethiopia . . . . . . . . . . . . . . . . . . . . .|17|
|5|Multinomial Logit Model - Malawi<br>. . . . . . . . . . . . . . . . . . . . .|18|
|6|Heckman Selection Model<br>. . . . . . . . . . . . . . . . . . . . . . . . . .|22|
|7|Panel Data Analysis<br>. . . . . . . . . . . . . . . . . . . . . . . . . . . . .|24|
|8|Months in Operation - by Location . . . . . . . . . . . . . . . . . . . . .|26|
|9|Reasons for Enterprise Exit<br>. . . . . . . . . . . . . . . . . . . . . . . . .|28|
|10|Prevalence of Non-Farm Enterprises in Rural Africa . . . . . . . . . . . .|39|
|11|Prevalence of Non-Farm Enterprises in Urban Africa<br>. . . . . . . . . . .|39|
|12|Summary Statistics - Probit Model . . . . . . . . . . . . . . . . . . . . .|40|
|13|Summary Statistics - Multinomial Logit Model . . . . . . . . . . . . . . .|42|
|14|Summary Statistics - Number of Observations by Age Category<br>. . . . .|43|
|15|Summary Statistics - Heckman Selection Model<br>. . . . . . . . . . . . . .|43|
|16|Summary Statistics - Heckman Selection Model<br>. . . . . . . . . . . . . .|44|
|17|Summary Statistics - Panel Data Analysis<br>. . . . . . . . . . . . . . . . .|45|

iv
In the remainder of this paper, we use the acronym “BD” for border discontinuity.

# **II. Historical and geographical background**

We first document the historical alignment of the boundaries around Cˆote d’Ivoire, drawing from the literature as well as from dedicated research in French colonial archives. This approach allows us to address the overlap assumption (random chance component of borders, see above).<sup>5</sup> Then, we document the geographical features of the boundaries. A few statistical tests assess the unconfoundedness assumption.

## **History**

The drawing of boundaries in West Africa was arbitrary, to a large extent (e.g., Hargreaves 1985), and very often divided pre-colonial political entities. Even structured kingdoms drew no maps, and they could be composed of groups that spoke different languages, such as the Gyaman kingdom across the border from Ghana (Terray 1982). Ethnic groups are historical objects that were at least influenced, if not constructed, by pre-colonial, colonial, and post-colonial politics (e.g., Amselle and M’Bokolo 1985, Posner 2005). Furthermore, the classification of ethnic names is not independent from the national political economy. Despite these caveats, we verified in available mappings (Murdock 1959; language maps from _Ethnologue_ : Lewis 2009) that the international boundaries we consider are not confounded by hard delimitations between ethno-linguistic areas.

> 5For the sake of space, we only present a brief summary of our historical investigation. A supplemental appendix on history is available at http://wber.oxfordjournals.org/.

12
The representativeness of the sample varies across countries (Plaza et al., 2011). The sample selected is nationally representative for Nigeria, Senegal, and Uganda. The survey is not, however, nationally representative for Burkina Faso and Kenya. For Burkina Faso, the sample is representative of the 10 provinces with the largest concentration of migrant sending households (or receiving remittances). For Kenya, the selected sample is representative of households in the top 17 districts with the largest concentration of households with migrant members. Data were collected from about 10,000 households for the five countries combined: 2,100 from Burkina Faso, 1,937 from Kenya, 2,245 from Nigeria, 1,833 from Senegal, and 1,870 from Uganda.

The questionnaire used for the data collection is standardized across all countries. Importantly, aside from questions that are common in household level surveys (e.g., pertaining to household composition, expenditures, living conditions, etc.) the questionnaire gathers information on migration and remittances. In particular, it asks whether households have a member who is currently residing abroad and if the migrant has sent remittances over the past 12 months. Also, the survey collects information on the amount of remittances received by the household and the method of transfer. Although there are other data sets containing information on households’ migrant status and the use of financial services (most notably the World Bank Global Findex database, see Demirguc-Kunt and Klapper, 2012) this data set is very rich and contains critical information that enables us to deal with potential econometric problems common to these types of studies. Particularly important is the fact that the questionnaire contains information on the migrants’ destination countries. This information allows the construction of instruments that can be used to control for the potential endogeneity of remittance inflows in the regression of the likelihood of opening a bank account.

5
when more than 1% of the total interviews were conducted in that year,<sup>16</sup> as confirmed by the Secretary of Health of each municipality, or with alternative data sources, such as newspaper articles, as specified in Appendix Table S1, which reflects the onset dates for the SISBEN by municipality.

In all of the specifications, we include year and municipality fixed effects to ensure that economy-wide changes, such as those in the minimum wage, taxes, or job security legislation, and time-invariant municipal characteristics do not influence our results. We are able to do this because there is variation in the implementation dates across municipalities. We also have specifications with controls at the individual level (including age group, schooling, marital status, sex, relation to the household head, and public sector employment); the household level (household composition characteristics specified in each table, proportion employed, average education, and average age); and labor market controls (municipal-level information for population, workingage population, economically active population, and employment; and state-level GDP). The labor market controls, which vary by state and year (and are thus unique to each municipality in our sample), should capture any differential effect of macroeconomic shocks across regions. Identification comes from assuming that the variable of interest is capturing variation due solely to the onset of the SISBEN interviews and that the characteristics likely to influence the informality rates of those municipalities that implemented the SISBEN interviews later do not differ from municipalities that implemented earlier.

We group the municipalities according to the onset in SISBEN interviews in early (1994) and late (1995) adopters. We explore the relationship between earlier and later adopters in Figure 1

> 16We do this to avoid coding an onset date that could be due to a data-entry error. For instance, in two municipalities, the onset date appears in 1994, but less than 0.01% of the total interviews for those municipalities were recorded in that year. For one of the municipalities, we confirmed the onset date with a newspaper article. For the other, we re-ran the results, changing the onset date to 1994. The results remained consistent with the results presented in Table 3. In a few cases, these dates differ from those recorded in the SISBEN database because municipalities initially conducted some interviews that were later overwritten with updated interviews. Because we are interested in the onset of interviews, we use the earliest date given by the officials.

11
Low-skilled labor migration from South Asia is temporary by design, as permanent stay in the main destinations of low-skilled migrants from the region is prohibited. The main host countries of migrants from South Asia in the GCC and Southeast Asia only grant temporary residence rights to low-skilled migrants. In the GCC countries, the acquisition of citizenship is effectively prohibited, irrespective of their duration of stay in the destination countries (Wahba 2015; Fargues 2011; Fargues and De Bel-Air 2015).<sup>3</sup> In addition, staying in the main destinations is strictly conditional on holding a valid employment contract, which is for a fixed time duration. Temporary migrants however have the possibility to renew their labor contract and work permit at destination, if there is demand for further work by the employer at destination (Bossavie, Gorlach, et al. 2021). An expiration of the employment contract without renewal, or a layoff by the employer automatically entails returning to the home country. In this institutional setting, a job loss or the expiration of the employment contract at destination is the main reason for returning.<sup>4</sup> In Bangladesh, Nepal and Pakistan, the average duration of stay overseas of return migrants estimated from surveys is 6.5, 2 and 1.8 years, respectively.

# **3 Data**

## **3.1 Household Survey Data**

Suitable microdata to carry out a detailed analysis of temporary and return migration is rare globally (Bossavie and Ozden 2022), including for South Asia. Administrative data at origin in Bangladesh, Nepal and Pakistan only records temporary migrants once they exit the origin country, but not when they return. The only microdata for South Asian countries that allow to identify return migrants are national household surveys. In those surveys, return migrants are captured at the time they have returned home after completing their most recent migration spell overseas. Such surveys in origin countries are suitable to carry out nationally-representative comparisons of the characteristics and labor market outcomes of return migrants with those of nonmigrants, including participation in labor market activities, type of employment after return and labor earnings. The drawback, however, is that the number of return migrants captured can be small due to the relatively low incidence of recent return migrants in the total country population, combined with the fact that survey questions

> 3According to the sponsorship system that regulates migration to the GCC, labor migrants can only enter and stay in the country through a sponsor, a local employer, which takes on both legal and economic responsibility for the migrant worker.

> 4The termination of employment abroad is reported as the main reason for returning home by labor migrants from Bangladesh and Nepal, according to the 2018/2019 World Bank Bangladesh Return Migrant Survey (BRMS), and to 2017/2018 Labor Force Survey for Nepal

6
In this study, we augmented an existing rich household panel of households (pre- and postprogram) with a new follow-up survey round. With the new round of data, we use a dynamic panel data approach estimated by GMM (Arellano and Bond 1991; Jalan and Ravaliion 1998) to test for impacts, which allow us to account for time-varying unobserved heterogeneity.

We find that most of the effects on household per capita expenditure, schooling enrollment, and transport costs, have indeed attenuated over time. Households in villages that received the project between 2001 and 2005, for example, experience stronger impacts for these outcomes compared to households that received the project earlier. However, attenuation in project effects is not across the board; for example, non-agricultural wage employment has risen more for households in areas targeted before 2001. This suggests a feedback effect between off-farm work and rural road development, where road improvements foster markets that become increasingly diversified across sectors. Alternatively, improved school enrollment, likely associated with better access to schools, could generate higher demand for labor in the long run. Ultimately, understanding these trends requires an analysis of other community-level changes in other infrastructure, such as electricity, and market growth to better understand how roads interact with other policy changes to affect households’ economic opportunities.

Distributional impacts of rural road investments are also an important policy concern. We examine these effects through (i) access, using data on distance to the project road and new data on quality of road paving and (ii) quantile regressions examining the effect of the road project by households’ initial position in the outcome distribution. We find that much of the overall project impact, including asset accumulation, is explained by road quality. The quantile results indicate that the initially poorest households have not shared in the benefits of rural roads; at most, households between the 25<sup>th</sup> and 50<sup>th</sup> percentile of per capita food expenditure have experienced positive gains. Households in this middle part of the distribution may be the most mobile in terms of changing 22