rafmacalaba/datause-encoder-data huggingface dataset:
**Table 3. Intention-to-treat effects on total investment in physical capital and land (past 6 months)**

||Total invest<br>|ment (log)<br>|
|---|---|---|
||All|Existing|
|Post|||
|Cash|0.160***|0.155***|
||(0.014)|(0.013)|
|Matching|0.004|0.008|
||(0.010)|(0.009)|
|ANCOVA|||
|Cash|0.151***|0.151***|
||(0.011)|(0.011)|
|Matching|0.010|0.010|
||(0.010)|(0.010)|
|Control group mean (in XOF, millions), all|0.029|0.027|
|Standard deviation|0.133|0.122|
|Control group mean (in XOF, millions),|0.154|0.273|
|investment>0|||
|Standard deviation|0.145|0.252|
|Standard error (S.E.) clustered|Municipality|Municipality|
|Controls|Yes|Yes|
|Block fixed-effects|Yes|Yes|
|R2 (Post)|0.142|0.128|
|R2 (ANCOVA)|0.122|0.122|
|N (Post)|1,032|960|
|N (ANCOVA)|952|952|

_Note:_ ***p < .01, **p < .05, *p < .1. Investment has been trimmed at the 99th percentile. All control variables come from the baseline survey. The list of controls include a dummy that equals one if the firm is already existing (vs new); dummies for whether the firm’s sector is industry or services, with agriculture being the reference; a share of correct recalls by the owner from a digit span memory test; a dummy that equals one if the owner is female; the age and number of years of professional experience of the owner; dummies for whether the owner is Christian or Muslim; dummies for whether the owner is of the two largest ethnic groups (Bissa or Mossi); dummies for whether the owner reads and writes French and a local language or reads and writes a local language only; dummies for whether the owner attained primary, secondary, or other education levels, with the excluded being no education level; the size of the owner household; the number of assets owned; and dummies for whether the owner is a household head and whether a member in the owner household is a community leader. The control group mean refers to the followup survey. The detailed regression results are provided in Table A.3 in the appendix.

18
Estimating the Economic and Distributional Impacts of the RCEP Figure 7. Real income gains by country in RCEP: Percentage change relative to business-as-usual scenario, 2035 <!-- Start of picture text --> 7.00<br>5.00<br>3.00<br>1.00<br>-1.00<br>Tariffs alone RCEP Roo liberalization Productivity kick<br>Vietnam Malaysia Cambodia China Thailand Indonesia Philippines Laos Korea Japan<br>Zealand<br>Australia and New<br><!-- End of picture text --> Source: World Bank staff calculations using ENVISAGE model.

Intra-RCEP trade increases significantly, even when only tariff reductions are considered (see Figure 8). When trade cost reductions and productivity increases are assumed, intra-RCEP trade increases by 12.3 percent in 2035 compared to BaU. Trade of RCEP member countries with the rest of the world falls, since trade from RCEP countries is now being redirected to RCEP members, especially under the scenario with a common set of rules of origin. A full RCEP scenario with productivity kick, however, would also increase exports to the rest of the world.

Figure 8. Impact on trade: Percentage change relative to ‘Business-as-Usual’ scenario, 2035 <!-- Start of picture text --> Intra RCEP trade<br>Exports to ROW<br>Imports from ROW<br>Intra ROW trade<br>-2.0 0.0 2.0 4.0 6.0 8.0 10.0 12.0 14.0<br>Tariffs alone RCEP ROO liberalization Productivity kick<br><!-- End of picture text --> Source: World Bank staff calculations using ENVISAGE model Exports and imports increase for all RCEP member countries under all RCEP scenarios. The increase in trade is higher when the full scenario with productivity kick is assumed. Under this scenario, the countries that experience a
**_Source:_** Author's own calculations based on PNAD 1999.

In sum, there appears to be evidence of a vicious cycle: children in poor households are less likely to attend school and be on-grade, and parents with low education have children who lag behind or do not attend school. This is evidence of anintergenerational transfer where children who are born into poverty are likely to continue being poor. The results show that there are economic barriers to educational attainment, and unless public interventions in the form of early assistance to educationally at risk children are made, these children will most likely never escape poverty.

# 6. **Summary and Conclusions**

Migration continues to be an important phenomenon in Brazil, and as many as 40 percent of Brazilians have migrated at some time in their lives. Northeast Brazil has historically been characterized as a source of migrant outflow, and most out migrants from the Northeast settled in the Southeast. The major migration routes in Brazil continue to be Southeast to Northeast and Northeast to Southeast. While the Northeast has recently undergone comparatively strong economic growth, large gaps between mean incomes and levels of living of the NE and SE persist. This paper sheds some light on the determinants of migration between regions and some of the impacts of migration decisions on households and regions.

The paper's findings show differences between migrants to the SE from the NE and migrants from the NE to the SE. These differences explain why the migration patterns emerge: different groups seek rewards in different areas. SE-NE migrants are on average poorer and less well educated than the Southeast average, while NE-SE migrants are financially better off and better educated than the Northeast average. This pattern is troublesome, as it signals that the economic divergence between the Southeast and the Northeast may grow as a result of migration.

The estimation of returns to migration provides insight into the changes in returns to migration over time. We **find** that a common feature in the predicted returns to migration is that the returns to migration are increasing with education for SE-NE mnigrants and decreasing for NE-SE migrants. We further find that returns to migration have been decreasing for NE-SE migrants and increasing for SE-NE migrants between 1995 and 1999. The predicted positive returns to migration for NE-SE migrants indicate that NE-SE migrants move to the SE in search of higher remuneration. The estimated lower returns to migration for Southeast to Northeast migrants provide only limited support for the human capital approach to migration and indicate that non-monetary factors may also have a role to play in SE-NE migration. Returning migrants to the Northeast may be due to adaptation 35
If a sufficient number of exporters target one market simultaneously, our results imply that their chances of surviving increase, possibly triggering a virtuous cycle of entry, survival and growth.

Like Eaton et al. (2008), we also find that export spells that survive tend to grow. For instance, in Senegal, products that entered a market in 2001 and survived till 2008 had reached, by then, four times their entry volume.

Lastly, export scale and scope at the firm level, by which we mean respectively destinations per product and products per destination, both evaluated at the time the firm launches a new product-destination combination, matter for its survival.

From a policy perspective, our findings could be construed as contributing to a possible rationale for using public funds to promote national exports abroad. The synergy we identify is akin to external economies, as the presence of competitors from the same countries exporting the same product to the same destination provides potential new entrants with information on the profitability of these exports ventures, help identifying potential buyers as well as provide information about the consumers’ preferences and therefore increases their likelihood of surviving. Similarly, this information becomes available to financial institutions and ease financial constraints of new exporters as shown by the fact that these ‘synergy effects’ are especially important for firms in sectors that are more dependent from the financial sector. However, these external economies may not be fully internalized by exporters as incumbents have not sufficient incentives to explicitly assist new entrants, leading to a market failure. Public intervention, in the form of export promotion, through market-product specific information and “matching with buyers” services, could possibly help overcome these market failures, although the record of publicly-financed export promotion is patchy, especially in developing countries (see Lederman, Olarreaga and Payton 2010), and the effects we identify even if statistically robust are quantitatively small.

The paper is organized as follows. Section 2 discusses the recent literature related to this paper. Section 3 presents qualitative evidence based on a recent survey of African exporters conducted by the World Bank that motivates the consequent analysis and presents a brief description of the data. Section 4 discusses the estimation strategy and the result, Section 5 3
**Table 7. Change in total electricity supply costs and its components for the 2018-2035 period due to the subsidy removal, cross-border trading and carbon constraint effects (%)**

|Cost<br>components|SR Im|pacts|C|BT Impacts||CEC I|mpacts|SR &<br>CBT|SR &<br>Impa|CEC<br>cts|CBT &<br>CEC|SR, CBT<br>& CEC|
|---|---|---|---|---|---|---|---|---|---|---|---|---|
||No CBT,<br>No CEC|CBT,<br>No CEC|SUB,<br>No CEC|No SUB<br>No CEC|SUB,<br>CEC|NO SUB<br>NO CBT|NO SUB<br>CBT|Impacts|NO<br>CBT|CBT|Impacts|Impacts|
||C2-C0|C3-C1|C1-C0|C3-C2|C5-C4|C4-C2|C5-C3|C3-C0|C4-C0|C5-C1|C5-C2|C5-C0|
|Capital cost|27.1%|40.3%|-5.1%|4.8%|-12.8%|56.6%|30.3%|33.1%|99.0%|82.9%|36.6%|73.5%|
|O&M cost|2.7%|5.6%|-1.4%|1.4%|-2.8%|7.9%|3.3%|4.2%|10.8%|9.1%|4.8%|7.7%|
|Fuel cost|16.8%|14.8%|-3.7%|-5.4%|-1.5%|-8.0%|-4.3%|10.5%|7.4%|9.9%|-9.4%|5.8%|
|Unmet D&R|0.4%|0.5%|-25.6%|-25.5%|-29.9%|19.8%|12.6%|-25.2%|20.2%|13.1%|-16.1%|-15.8%|
|cost|||||||||||||
|Subsidy cost|-100%|-100%|5.7%|-|-|-|-|-100.0%|-100%|-100%|-|-100%|
|Total cost|-1.5%|-2.1%|-6.0%|-6.7%|-9.2%|7.1%|4.1%|-8.0%|5.5%|1.9%|-2.8%|-4.2%|

SR – Subsidy removal, CBT – Cross-border trading, CEC – Carbon emission constraining, SUB - Subsidy 1
# **_4. Conclusions_**

Empirical estimates of the size of the economic shocks triggered by the pandemic and lockdown evolution have been scarce since official economic indicators are only made available with a significant lag. In this paper, we provide an illustration by tracking the evolution of high-frequency variables, related to the trends in public transit data as well as traffic congestion.

The availability of such high-frequency variables allows us to investigate the economic impact of lockdowns, which have been the main public health policy implemented by governments around the world to contain the pandemic. It has been argued that these measures, which are useful in “flattening the curve” of health costs, may come at high economic costs, but also that a silver lining of reduction in congestion is only a temporary result, reverted by the increase in motorization, The analysis in this paper also confirms the significant cost of such measures in terms of reduction in urban mobility in terms of public transport and traffic congestion. Interestingly, it shows that beyond the impact of the lockdowns, also the spread of the disease itself has an economic impact distinct from that of lockdown measures, as the incidence of death per population increases the drop in activity associated with the spread of the disease, and is statistically highly significant, both in terms of reduction in urban mobility by public transport and a reduction in traffic congestion.

We also find different results on the magnitude of impact of cross sectoral vis-à-vis sectoral restrictions on urban mobility and congestion. Whereas the magnitude of the spread of the disease is higher than the overall stringency of the lockdown, we turn to introduce sector specific restrictions and, as expected, results are significantly different. The impact of restrictions on public transit is much higher than the spread of the disease and acts indirectly as a disincentive also to move on the road. More effective safety measures, such as those related to the use of facial covering are associated with higher use of public transport and an increase in the likelihood of low congestion.

We find no evidence of intermodal competition between public transportation and road transport. In particular, the expansion of car registration has not led to a decrease in public transport mobility. Increasing car registration has been, however, significantly associated with an increase in traffic congestion, particularly in mega-cities.

Since the beginning of the pandemic, most public transport operators have quickly stepped up and taken concrete action to make transit systems COVID-safe for staff and passengers. But despite the best efforts of professionals across the sector, as testified by the positive impact of enhanced safety measures, the COVID-19 crisis has dealt a massive blow to public transport, which has not recovered to the pre-Covid levels. On the other hand, congestion has been ramping up, also fueled by the expansion in new car registration.

23
indicative as it is based on simple cross-sectional correlations that cannot account for possible selection of more socially enterprising individuals into better jobs and social networks. Another more robust analysis of Indonesian survey data that tracked people’s level of community participation over time found that men and women who became unemployed between 2000 and 2007 were less likely to be involved in community activities (controlling for personal attributes). In some locations this result was observed in spite of a general increase in community activities, indicating that individual and household effects of unemployment can influence personal social outcomes, independent of social and community contexts (Giles et al. 2012).<sup>14</sup> Related survey analysis from the US indicates also provides support for the hypothesis that workplaces involve greater exposure to people of dissimilar perspectives or race than family relations or social activities (2013, Mutz and Martin 2001, Mutz and Mondak 2006). Kilroy (2011) finds in Trinidad and Tobago that for between 81 and 41 percent of study respondents work brought them into contact with people of a wider range of races than their social life. These studies also tend to show positive effects of work-place interactions on political tolerance and dialogue, even though the usual concerns about selfselection and measurement error apply (see below, Mutz and Mondak 2006, Banaszak and Leighley (1991).

Anecdotal evidence and ethnographic studies suggest that professional and commercial interactions can reduce the risk of conflicts between groups that usually entertain tense relations (Dani et al. 1999, Kilroy 2012, see also Pickering 2006). A widely cited study by Varshney (2002) found that economic interdependence of Muslims and Hindus through business organizations, trade unions, political parties, and professional associations, helped avert outbreaks of ethnic violence in some Indian cities, while ethnic riots were more frequent in less interdependent cities. Survey research from Bosnia and Herzegovina and the former Yugoslav Republic of Macedonia, documents that the work place constituted the most important area for inter-ethnic cooperation, ahead of neighbourhoods and local social associations (Dani et al. 1999, Pickering 2006, WDR 2013: 15). Other examples of business relations between Greeks and Turks during the war in Cyprus, or smuggling between

> 14 Note however, that this study could not account for possible time variant personal attributes that could simultaneously influence respondents’ employment status and their social relations. Examples are illness or other short-term economic shocks.

17
**Table 1: Student characteristics and family background**

|||Dev7 co|untries|Viet|nam|
|---|---|---|---|---|---|
|Variable|Description|MS|Valid N|MS|Valid N|
|**Fixed charact**|**eristics**|||||
|FEMALE|Sex of student|0.5265<br>(0.4993)|41394|0.5336<br>(0.4989)|4882|
|AGE|Age of student|15.8211<br>(0.2895)|41394|15.7692<br>(0.2885)|4853|
|**Student’s prio**|**r history**|||||
|PRESCHOOL|Attended Preschool|0.7888<br>|40114|0.912<br>|4866|
|||(0.4082)||(0.2833)||
|REPEAT|Grade repeating|0.1915<br>(0.3935)|40343|0.0679<br>(0.2516)|4860|
|**Truancy from **|**School**|||||
|ST08Q01|Times late<br>for school|1.5131<br>(0.7648)|40663|1.1872<br>(0.4685)|4873|
|ST09Q01|Days unexcused<br>absence|1.2192<br>(0.5276)|40650|1.0999<br>(0.3527)|4875|
|ST115Q01|Times skipped<br>classes|1.2585<br>(0.545)|40632|1.0764<br>(0.3216)|4880|
|**Parental back**|**ground and family we**|**alth**||||
|HISEI|Highest parental<br>occupational status|40.4196<br>(22.5168)|32814|26.6023<br>(19.855)|4860|
|MISCED|Educational level<br>of mother (ISCED)|3.1193<br>(1.9853)|40486|2.1744<br>(1.6059)|4844|
|WEALTH|Family wealth<br>possessions|-1.4606<br>(1.2267)|40821|-2.1343<br>(1.1656)|4881|
|CULTPOS|Cultural possessions|-0.1424<br>(0.9678)|39905|-0.2361<br>(1.0173)|4809|
|HEDRES|Home educational<br>resources|-0.7427<br>(1.1473)|40579|-1.0743<br>(0.9364)|4874|
|BOOK<br>~~N~~|Number of books|53.6393|39631|50.786|4841|
||in family home|(94.5556)||(75.4031)||

Notes: The variables relate to the questionnaires administered to students in the general (non-rotated) booklet. For a more detailed description of variables, please see Tables A2, A3, A4 in the Appendix.The variable means of Dev7 and Vietnam are statistically different at the 95% significance level, except FEMALE. Figures in parenthesis represent standard deviations.

The findings regarding PRESCHOOL and REPEAT indicate the possible importance of the trajectory of the student prior to high school. Repetition rates are difficult as comparative indicators of system quality because of the variations across countries in curriculum and standards, but REPEAT is another interesting variable to keep in mind as a possible clue to the mystery of Vietnam’s PISA performance. As in some other East Asian cultures, Vietnamese parents expect their children to study hard. Though Mark Twain, translated into Vietnamese, is quite a best seller for young readers in Vietnam, truancy from school is not perceived benevolently by parents.<sup>2</sup> Table 1 indicates a consistently lower truancy rate

> 2A cultural explanation is possibly quite important in explaining Vietnam’s anomalous PISA results, though the PISA data set may only be able to measure the possible effects of culture rather than measuring cultural differences. Literature from the World Values Survey, that does seek to measure cultural differences,

6
# **Figure 4: FEATS study design as per original protocol**

|**Group A**<br>:|**Group B**<br>:|
|---|---|
|216 ART households receiving<br>_treatment and support provided in_<br>_the existing programme_|216 ART households: A +<br>_bi-weekly visits by trained ARV_<br>_peer adherence supporter (PAS)_|
|**Group C**<br>:|**Group D**<br>:|
|216 ART households: A + B +<br>_nutritional supplement (canned_<br>_food) delivered by PAS_|208 comparison households<br>randomly sampled from the<br>relevant communities|

20
0.401 to 0.399.<sup>47</sup> We also find that rural households gain less than urban households because the wage rate of skilled labor increases more than the other factors of production and rural households are less endowed with skilled labor than urban households.

Figure 4.2 shows there is a distribution of income changes across the 55,098 households that is centered around a mean gain of income of 7.3 percent.<sup>48</sup> Except for fourteen households, we estimate that all households will gain in the new equilibrium relative to the status quo.<sup>49</sup> These results highlight in a real household model what we showed in section 2 in a stylized model: with Dixit-Stiglitz productivity impacts, we escape the pessimism of Stolper-Samuelson. The fact that the model has FDI in business services yields Dixit-Stiglitz productivity effects from services liberalization and further contributes to increase in real returns to all mobile factors. The principal caveat to the widespread gains is the potential loss of income to owners of specific capital in sectors subject to increased competition from FDI.<sup>50</sup> In figure 4.2 we compare the results across 55,098 households based on two models. On the right side of the diagram, is the histogram of results for all households from our central model. Despite diversity among households, virtually none are estimated to lose. On the left side of the diagram, we present the histogram of results for all households with a constant returns to scale model. The CRTS

> 47 Our estimate of the Gini coefficient is virtually identical to Rostat (the statistical agency of Russia). Rostat estimates the Gini coefficient at 0.400 in 2003.

> 48 Of the 55,098 households, there are 59 with gains less than two percent and 7 households with gains above 25 percent. Thus, 99.9 percent of all households have gains that fall in the range of 2 to 25 percent. Fourteen households, or twohundredths of one percent of the households, are estimated to lose.

> 49 Households that depend disproportionately on specific capital that falls in return would be expected to lose from WTO accession, but the data do not allow this distinction.

> 50 While households that are heavily endowed with specific capital in declining sectors will lose on average from WTO accession, those who can form joint ventures with foreign investors will likely see the value of their specific capital holdings increase.

71
# **Informing Migration Policies: A Data Primer**<sup>**1**</sup>

_Calogero Carletto, Jennica Larrison and Çaglar Özden_<sup>_2_</sup> JEL classification: F22, J61, O15, Keywords: migration, development, survey design, data collection, data dissemination

> 1 A revised version of this paper is forthcoming as chapter 2 in _International Handbook of Migration and Economic Development_ , edited by Robert E. B. Lucas and published by Edward Elgar Publishing, Inc. in 2015.

> 2 Part of the analysis in the paper has been generously supported by the Knowledge of Change Program (KCP)