rafmacalaba/datause-encoder-data huggingface dataset:
# **_Domain_**

Globalization in the majority of papers is defined as increased openness in trade policy. Only two papers (Milanovic, 2005a, and Behrmann, Birdsall and Szekely, 2003) look at capital account liberalization. None define globalization in terms of increased labor mobility. And with respect to inequality, both global inequality (inequality between all citizens in the world) and within-country inequality receive attention in the context of multi-country studies. Interestingly, there is also a significant group of studies that focus on wage-inequality, typically in the context of single-country studies exclusively in Latin America.

Clark, Dollar and Kraay (2001) is a good example of a paper dealing with global inequality. They find that inequality between the population-weighted mean incomes of the globalizing countries has decreased substantially between 1975 and 1995. This reflects increases in average incomes in key developing countries (especially China) relative to the OECD average, so that population-weighted between-country inequality has fallen. At the same time, they point to rising within-country inequality for this group of countries but not enough to offset the (population-weighted) between-country decline. This appears to be a fairly firm result but does not of course incorporate the countries that have not globalized. Here, Clark, Dollar and Kraay point to decline and divergence. Since these are some of the poorest countries in the world, this is clearly a matter of concern. Nevertheless, they show that inequality worldwide has declined between the second half of the seventies and the second half of the nineties despite these disparate experiences.

If, however, one looks at the world as a whole, the conclusions regarding global inequality (inequality between all individuals) are not so firm. Milanovic (2005) finds a sharp increase between 1988 and 1993, and then a moderate decline during the next five-year period. Sala-i-Martin (2002) and Bhalla (2002) argue that global inequality has gone down although their approach which combines household survey and national accounts (GDP data) is questionable, and the results may be driven by a number of 6
ments, Econometrica, 65: 557-586.

Stock, W.H. and M. Yogo (2002): Testing for weak instruments in linear IV regression, NBER Technical Working Paper 284, Cambridge.

Stark, O. and Y. Wang (2002): Inducing human capital formation: migration as a substitute for subsidies, Journal of Public Economics, 86(1): 29-46.

Vidal, J-P. (1998): The e¤ect of emigration on human capital formation, Journal of Population Economics, 11, 4: 589-600.

# 6 Appendix

## 6.1 Proof of Proposition 1

Using At+1 = nAt(1 � mt+1), the planner maximizes $<sup>FB</sup> in (5) with respect to ht+1, et and mt+1. This gives the following ...rst order conditions: Focusing on the steady state, the social return to human capital, �<sup>FB</sup> ss<sup>=w[1</sup> 1<sup><u>+</u></sup> �<sup><u>�</u></sup> �n�<sup>nRwa]</sup> , exceeds the private return on human capital, w.

From the ...rst order condition with respect to mt+1, emigration deteriorates social welfare. The optimal emigration rate is therefore zero.

## 6.2 Proof of Proposition 2

At the steady state, the ...rst order conditions of (8) are: 28
al. 2002). Berry (2009), examining the assignment of incentives to the parent or the child on a specific reading goal in India, finds that the incentives to the child may be more effective if the children have less productive parents and lower initial test scores. Finally two programs, in Bangladesh and Colombia, make transfers to a Bank account in the student’s name, which can be accessed by the student later, but no evaluation of this aspect of these programs is available. It seems plausible that paying at least a portion of the transfers to young people – either directly or into a savings account – may be worth considering.

Pilot programs in Burkina Faso, Morocco, and Yemen all have randomized treatment arms for making transfers to women/mothers vs. men/fathers. To our knowledge, no study other than the one presented in this paper explicitly evaluates the effect of making some of the payment (in the context of a cash transfer program conditional on school attendance) to the young person (student) vs. the parents/guardians.

# **3. SURVEY AND RESEARCH DESIGN**

# _3.1. Study setting and sample selection_

Malawi, the setting for this research project, is a small, poor country in southern Africa. Its population of almost 14 million in 2007 is overwhelmingly rural, with most people living from subsistence farming supplemented by small-scale income-generating opportunities that are typically more available to men than they are to women. The country is poor even by African standards: the GNI per capita (PPP, current international $) is $750 in 2007, compared to an average of $1,870 for Sub-Saharan Africa (World Development Indicators Database, 2008).<sup>6</sup> Zomba district in the Southern region was chosen as the site for this study for several reasons. First, it has a large enough population within a small enough geographic area rendering field work logistics easier and keeping transport costs lower. Zomba is a highly populated district, but distances from the district capital (Zomba Town) are relatively small. Second, characteristic of Southern Malawi, Zomba

> 6 Using the Atlas method, The GNI per capita (in current US$) in Malawi is 250 in 1997, compared with 952 in SubSaharan Africa as a whole.

8
# Stigma and the Take-up of Social Programs<sup>†</sup>

Jacques Ewoudou<sup>~~t~~</sup> Clarence Tsimpo<sup>=Y</sup> Quentin Wodon<sup>L=</sup>

## JEL Codes: C72, I38, H53

Key Words: social stigma, program participation, supermodular games, Nash-equilibria.

> ~~t~~ Jacques Ewoudou: Statistics Canada, Ottawa, K1A OT6, Canada. Email: Jacques.ewoudou@statcan.ca.

> =YClarence Tsimpo: World Bank, 1818 H Street NW, Washington DC, 20433, USA. Email: Ctsimponkengne@worldbank.org.

- L=Quentin Wodon: World Bank, 1818 H Street NW, Washington DC, 20433, USA.

Email: Qwodon@worldbank.org.

†The views expressed here are those of the authors and do not necessarily represent the views of the World Bank, nor those of its Executive Directors or the governments they represent. This paper is part of a larger work program on Social Exclusion at the Development Dialogue on Values and Ethics in the Human Development Network at the World Bank.
In Mozambique, as in other places, there is segmentation along educational levels and type of employment. Those employed in the public sector are generally the best educated, with the private sector and owners of micro enterprises being the second most educated, while family farmers are the least educated. Those working in the HE sector are usually situated between family farmers and the private wage sector (figure 3).

**Figure 3 Type of employment and distribution of education, 2009**

<!-- Start of picture text --> 100 Complete upper secondary or<br>90 above<br>80<br>Complete lower secondary<br>70<br>60<br>Complete upper primary<br>50<br>40<br>30 Complete lower primary<br>20<br>10 Incomplete primary<br>0<br>No education<br><!-- End of picture text --> Source Authors’ calculations, IOF 2008 /09

# **Characteristics of the HE sector**

Although Mozambique is still primarily a rural country, nationally representative surveys report that about half of HEs were located in urban areas in 2008. The majority of HE owners report that they operate their enterprises from their own home, with public markets being the second most common location (about 30%). Urban HE owners usually report that the enterprise is their primary income earning activity; 65 percent of urban HEs report operating their business around the year, compared with 50 percent in rural areas. Most are traders or providing services such as hair dressing or making and producing low cost items needed by other households such as bricks, furniture, beer, or charcoal. According to the RICs 2010 data on rural and peri-urban HEs, almost all HEs sell their goods and services to households (table A3), and buy their inputs from small traders. Most HEs had been created in the last five years, and one-quarter of HEs were less than one year old (table A1).

Although HEs do generate many new jobs, it is mainly through establishment of new HEs as oppose to new hiring within HEs. Over 80 percent of HEs are operated by owners by themselves (table A1), without even a family member assisting. Ninety-six percent are operated by a single individual with or without family help, while only 4 percent of HEs reported hiring any help outside the family (IOF 2008/09). This is consistent with evidence from other low-income countries, which also shows that most HEs start as a small one-person enterprise and stay that way. Few HEs expand into employment beyond the household, growing into micro or even small enterprises. This is the experience from Ethiopia (Loeninng and Imru, 2009), Tanzania (Kinda and Loening, 2008), Madagascar (Grimm 2011), and other 7 | P a g e
companies of Ukraine, Tanzania, Egypt, and Pakistan’s KE also show curves that bow more noticeably outward than the rest of the sample.

**Figure 9: Level of Cross-Subsidization**

<!-- Start of picture text --> Colombia<br>Philippines (BENECO)<br>Philippines (MERALCO)<br>Rajasthan (AVVNL)<br>Rajasthan (JDVVNL)<br>Rajasthan (JVVNL)<br>Vietnam<br>Kenya<br>Senegal<br>Uganda<br>Peru (Luz del Sur)<br>Peru (Hidrandina)<br>Odisha (WESCO)<br>Odisha (CESCO)<br>Pakistan (LESCO)<br>Pakistan (KE)<br>Egypt<br>Tanzania<br>Ukraine<br>(Dniprooblenergo)<br>Ukraine<br>(Khmelnytskoblenergo)<br>AP (APSPDCL)<br>AP (APEPDCL)<br>AP (APNPDCL)<br><!-- End of picture text --> _Note:_ Data for latest available year. _Source_ : World Bank staff.

42. Comparing the level of cross-subsidization to realized full-cost recovery (C3) suggests that there is a limit to ‘acceptable’ levels of cross-subsidization at around 15 percent, using the quantification of cross-subsidization defined above. Specifically, the analysis suggests that no country with a crosssubsidization indicator above 15 percent comes even close to full-cost recovery (all five cases are below 80 percent). Pakistan, Egypt, Tanzania, and Andhra Pradesh all feature large cross-subsidies and low-cost recovery; the opposite is true for Colombia, the Philippines, and Vietnam. The converse is not true; Rajasthan in particular is an exception, with low cross-subsidization and low-cost recovery. This suggests that limiting cross-subsidization is a necessary but not sufficient condition to achieve cost recovery.

43. Cross-subsidization specifically aimed to protect the poor often takes the form of so-called ‘lifeline’ tariffs up to a certain number of kWh per month. Notably, several of the countries with the highest lifeline tariff block thresholds, such as Colombia (130-173 kWh), the Philippines (100 kWh), and Ukraine (100 kWh or higher, based on household type) are all on the high end of cost recovery. This suggests that targeted cross-subsidies to protect the poor can be part of a sustainable electricity pricing strategy.

44. Another form of cross-subsidization is to maintain uniform tariffs across the country across regions and distribution companies with differences in cost of service. These forms of implicit cross23
<mark>we should not expect any impact or a much lower impact on HAZ or stunting and we indeed find that there is no impact (Test 1, Table 5).</mark><sup>19</sup> <mark>Second, we restricted the sample to those who migrated before 2001 (during the refugee shock) but living very far away from refugee camps (in clusters/regions that are located in the South-East of the country). For this group, we should expect a much lower impact than for those who migrated in regions closer to the camps and indeed we find no significant effect (Test 2, Table 5). We also follow Baez (2011) by restricting the sample to those who were residing in Northwestern Tanzania</mark><sup>20</sup> <mark>distinguishing between geographical clusters of high refugee intensity and low refugee intensity and we find as expected significant results for high refugee intensity clusters (“North-West” in Table 5) in line with our results in Tables 3 and 4. Therefore, distance from the refugee shock is indeed a critical factor to understand the relation between child stunting and mothers’ exposure to shocks.</mark> <mark>Third, we checked whether results are not driven by a “</mark> _<mark>cohort effect</mark>_ <mark>” by comparing anthropometrics of children from mothers born after 1993 to those born before 1993 without introducing the distance to a refugee camp (Test 3, Table 5). Also, in this case we found no statistically significant differences between the two groups in terms of their children’s anthropometrics. This confirms that the cohort effect found in Tables 3 and 4 is determined by the distance from camps (the refugee crisis).</mark> <mark>Fourth, we made use of the 1990/1991 pre-refugee arrival DHS survey to study whether parents/children living further away from refugee camps were already better or worse off prior to refugees’ arrival in late 1993. Again, we find no statistically significant differences between health anthropometrics of under five years old children from communities of refugees hosting areas and non-refugees hosting areas (Test 4, Table 5).</mark> <mark>Finally, the extent to which other events in Tanzania such as natural disasters (namely the 1998</mark> _<mark>el nino</mark>_ <mark>flooding and the 1999 drought) might have undermined socio-economic conditions of mothers during refugee influx and subsequently their children’s health outcomes in 2015/2016 is dismissed. The</mark> _<mark>el nino</mark>_ <mark>flooding of 1998 followed by the severe drought in 1999 mainly hit the central and northeastern part of the country (Arusha, Manyara, Shinyanga, Simiyu, and Dodoma regions), all areas that did not experience the massive inflow of refugees in the country.</mark>

> 19 Recall that for households declaring to have moved to the current location after 2001 we cannot identify their location during the refugee crisis. We can consider this group as a random sample in terms of distance from refugee areas. 20 Biharamulo, Muleba, Bukoka Rural and Bukoka Urban.

19
> 43 Koks, E.E. et al. A global multi-hazard risk analysis of road and railway infrastructure assets. _Nat. Commun._ **10** , 2677 (2019).

> 44 Tolman, H. L. _User Manual and System Documentation of WAVEWATCH III_ TM version 3.14. Technical note, MMAB Contribution 276, 220 (2009).

45 Vousdoukas, M.I. et al. Global probabilistic projections of extreme sea levels show intensification of coastal flood hazard. _Nat. Commun._ **9** , 2360 (2018).

46 Vousdoukas, M. I. et al. Developments in large-scale coastal flood hazard mapping, _Nat. Hazards Earth Syst. Sci_ . **16** , 1841–1853 (2016).

> 47 Scussolini et al. FLOPROS: an evolving global database of flood protection standards. _Nat. Hazards Earth Syst. Sci._ , 16, 1049–1061 (2016).

48 Marconcini, M., Metz-Marconcini, A., Esch, T. & Gorelick, N. Understanding current trends in global urbanisation – the World Settlement Footprint suite _. GI_Forum,_ **9** (1), 33–38 (2021).

49 The WSF-Evo dataset and its technical documentation are available from DLR at <u>https://www.dlr.de/content/de/artikel/news/2021/04/20211111_blick_aus_dem_weltraum_so_entwickeln_sich_ staedte.html</u> 50 Marconcini, M., Metz-Marconcini, A., Esch, T. & Gorelick, N. Understanding current trends in global urbanisation – the World Settlement Footprint suite _. GI_Forum,_ **9** (1), 33–38 (2021).

> 51 Liu, X., Huang, Y., Xu, X. _et al._ High-spatiotemporal-resolution mapping of global urban change from 1985 to 2015. _Nat Sustain_ **3,** 564–570 (2020).

52 Fang, J., Zhang, C., Fang, J., Liu, M. & Luan, Y. Increasing exposure to floods in China revealed by nighttime light data and flood susceptibility mapping. _Environ. Res. Lett._ **16** , 104044 (2021).

> 53 Ying, X., Bing, Z., Bo-Tao, Z., Si-Yan, D., Li, Y, and Rou-Ke, L. Projected flood risks in China based on CMIP5. _Adv. Clim. Chang. Res._ **5** (2), 57–65 (2014).

> 54 D'Errico, J. _SLM - Shape Language Modeling_ (MATLAB Central File Exchange, 2021).

https://www.mathworks.com/matlabcentral/fileexchange/24443-slm-shape-language-modeling 55 Dilling, L., Prakash, A., Zommers, Z., Ahmad, F., Singh, N., de Wit, S., Nalau, J., Daly, M., Bowman, K. Is adaptation success a flawed concept?. Nat. Clim. Chang. **9,** 572–574 (2019).

Economy-level summary statistics 22
**-** **<u>Table 4.  Gravity Estimates   (2 stage HMR procedure)</u>**

||1a|1b|2a|2b|3a|3b|4a|4b|
|---|---|---|---|---|---|---|---|---|
||outcome|selection|outcome|selection|outcome|selection|outcome|selection|
|Distance (log)|-1.121|-0.439|-1.126|-0.435|-1.091|-0.417|-1.129|-0.434|
||[0.024]***|[0.062]***|[0.024]***|[0.062]***|[0.024]***|[0.061]***|[0.024]***|[0.063]***|
|GDP Importer (log)|0.883|0.253|0.895|0.247|1.002|0.323|0.856|0.219|
||[0.027]***|[0.049]***|[0.027]***|[0.049]***|[0.015]***|[0.030]***|[0.028]***|[0.046]***|
|GDP Exporter(log)|0.816|0.228|0.819|0.226|1.214|0.391|0.825|0.221|
||[0.029]***|[0.046]***|[0.029]***|[0.046]***|[0.013]***|[0.031]***|[0.031]***|[0.047]***|
|Population Importer (log)|0.122|0.034|0.124|0.028|0.053|0.012|0.146|0.076|
||[0.023]***|[0.037]|[0.023]***|[0.037]|[0.019]***|[0.034]|[0.025]***|[0.039]*|
|Population Exporter (log)|0.261|0.016|0.26|0.017|0.011|-0.03|0.25|0.033|
||[0.024]***|[0.037]|[0.024]***|[0.037]|[0.016]|[0.033]|[0.026]***|[0.040]|
|LandlockedImporter|-0.049|0.104|-0.069|0.119|0.018|0.144|-0.035|0.169|
||[0.056]|[0.091]|[0.056]|[0.091]|[0.055]|[0.086]*|[0.058]|[0.099]*|
|Landlocked Exporter|-0.202|-0.093|-0.201|-0.092|-0.013|0.018|-0.157|-0.007|
||[0.057]***|[0.091]|[0.057]***|[0.091]|[0.056]|[0.083]|[0.062]**|[0.107]|
|Common border|1.256|0.376|1.248|0.369|1.251|0.431|1.234|0.472|
||[0.142]***|[0.448]|[0.141]***|[0.456]|[0.142]***|[0.468]|[0.142]***|[0.429]|
|Common language|1.319|0.865|1.317|0.869|1.284|0.861|1.319|0.913|
||[0.074]***|[0.250]***|[0.074]***|[0.249]***|[0.072]***|[0.251]***|[0.074]***|[0.257]***|
|TTRI|-1.319|-0.302|||-1.314|-0.34|-1.373|-0.337|
||[0.368]***|[0.148]**|||[0.356]***|[0.145]**|[0.378]***|[0.149]**|
|NTB-RI|0.993|-0.932|||0.698|-1.106|0.917|-0.986|
||[0.312]***|[0.411]**|||[0.312]**|[0.384]***|[0.309]***|[0.417]**|
|OTRI|||-0.692|-0.404|||||
||||[0.185]***|[0.161]**|||||
|LPI Importer|0.367|0.298|0.332|0.326|||0.379|0.267|
||[0.071]***|[0.145]**|[0.071]***|[0.144]**|||[0.075]***|[0.142]*|
|LPI Exporter|1.177|0.882|1.173|0.881|||1.219|0.823|
||[0.073]***|[0.158]***|[0.074]***|[0.158]***|||[0.074]***|[0.158]***|
|DB Import Costs (log)|-0.271|-0.213|-0.291|-0.204|-0.383|-0.277|-0.22|-0.124|
||[0.050]***|[0.091]**|[0.050]***|[0.090]**|[0.046]***|[0.083]***|[0.052]***|[0.095]|
|DB Export Costs (log)|-0.367|-0.207|-0.364|-0.207|-0.646|-0.38|-0.332|-0.145|
||[0.051]***|[0.090]**|[0.051]***|[0.090]**|[0.047]***|[0.079]***|[0.052]***|[0.103]|
|Entry costs indicator.||-0.198||-0.209||-0.183||-0.187|
|||[0.086]**||[0.085]**||[0.085]**||[0.087]**|
|# documents to export|||||||0.064|0.017|
||||||||[0.015]***|[0.018]|
|Days to export|||||||-0.006|-0.007|
||||||||[0.003]**|[0.003]*|
|# documents to import|||||||0.036|0.001|
||||||||[0.013]***|[0.021]|
|Days toimport|||||||-0.01|-0.01|
||||||||[0.002]***|[0.003]***|
|Constant|-29.803|-6.253|-29.878|-6.178|-30.44|-6.243|-30.58|-6.842|
||[0.697]***|[1.331]***|[0.698]***|[1.327]***|[0.698]***|[1.345]***|[0.705]***|[1.370]***|
|Observations|10508|10508|10508|10508|10725|10725|10508|10508|

_Note_ : * significant at 10 percent; ** significant at 5 percent; *** significant at 1 percent. Robust standard errors are in brackets.

27
(see detailed classifications in Appendix Table 1).

The classification of country groups is provided in Appendix Tables 2 and 3.

Source: Based on partner data from UN COMTRADE Statistics.

Table 3 shows the agricultural trade status of the same group of countries.<sup>15</sup> As expected, addition of the non raw food agricultural commodities changes the picture significantly. With the raw food definition only 52 developing countries were net food exporters. With the broader definition, 75 developing countries are net agricultural exporters. Changes are minimal within the middle-income countries. Excluding the oil, island and conflict countries, number of middle-income countries that are net agricultural exporters only increase from 27 to 28.

The change in low-income countries, however, is very significant. Only 16 of the 58 countries were net exporters with a narrow definition of food. With the broader definition of all agriculture, 34, or more than half the low-income countries become net agricultural exporters. If the oil exporters and conflict countries are excluded, then 28 of 43 low-income countries are net exporters of agricultural commodities. Thus, even in terms of numbers, most of low-income countries are net agricultural exporters, and would benefit from an increase in agricultural prices.

**Table 4: Trade Balance for All Agriculture by Income Group Country**

||Net Exp<br>|orts/Imports<br>|($million<br>|)<br> <br>|Net Exp <br>|orts as % of<br>|All Goods<br>|Imports<br>|
|---|---|---|---|---|---|---|---|---|
|CountryGroup (No. of Country)|1980/81|1990/91|2000/01|2004/05|1980/81|1990/91|2000/01|2004/05|
|World (196)|8,385|24,946|32,743|28,974|0.5|0.8|0.5|0.3|
|Industrial Countries (33)|3,534|6,127|13,600|2,141|0.3|0.3|0.3|0.0|
|Middle-income, All (105)|7,319|15,663|13,091|20,098|2.0|2.2|0.8|0.7|
|Mid Income--Oil Exporters (20)|-6,079|-5,120|-5,436|-8,595|-5.7|-5.3|-2.7|-2.2|
|Mid Income--Civil Conflict (4)|413|65|-422|-660|12.1|1.7|-2.6|-2.5|
|Small Island Economies (30)|110|52|-134|-262|1.8|0.6|-0.7|-1.0|
|Other Middle-income (51)|12,875|20,665|19,083|29,616|5.2|3.5|1.3|1.2|
|Low-income, All (58)|7,728|8,656|9,994|11,737|13.7|13.5|7.5|4.8|
|Low-income--Oil Exporters (7)|809|1,024|424|209|4.1|8.8|2.3|0.6|
|Low-income--Civil Conflict (8)|604|315|-71|-190|10.8|4.7|-0.9|-1.7|
|Other Low-income(43)|6,315|7,317|9,642|11,718|20.5|16.0|9.1|5.9|

Notes: All agriculture is defined as all raw food, cash crops and agricultural raw materials in SITC Revision 2, excluding processed food and seafood products (see detailed classifications in Appendix Table 1). The classification of country groups is provided in Appendix Tables 2 and 3.

Source: Based on partner data from UN COMTRADE Statistics.

15 We should point out that many countries move from being a net importer of food to an exporter of agricultural commodities and vice versa. For example, among large countries, Indonesia moves from a food deficit to an agricultural surplus country, while China has a large trade surplus in raw food but has a trade deficit in all agriculture.

10