rafmacalaba/datause-encoder-data huggingface dataset:
41. N. Arevalo-Jimenez in _Populacoes Humanos e Desenvolvimento Amazonica_ (Serie Cooperacao Amazonia, #3, Organization of American States, Belem, Brazil, 1989) pp. 313-332.

42. J. Jones, _Development Anthropology Network Bulletin of the Institute for Development Anthropology_ 8 (2), 1-8 (1990).

43. P. West and S. Brechin, in _Resident Peoples and National Parks,_ P. West and S. Brechin, ads. (University of Arizona Press, Tucson, AZ, 1991), pp. ?.

44. P. Herlihy, _Cultural Survival Quarterly,_ 13 (3), 17-24 (1989).

45. M. Chapin and P. Breslin, _Grassroots_ Development, _Journal of the InterAmerican Foundation,_ 8 (2), 26-35 (1986?).

46. Presidencia de la Republica, Colombia, Fuero _Indigena Colombiano_ (R. Roldan, ed., Gov't. of Colombia, Bogota, 1990).

47. Republic of Colombia, Policy _of the National Government in Defense of the Rights of Indi enous Peoples_ and _the Ecological Conservation of the Amazon_ (Gov't. of Colombia, Bogota, 1990). 48. Commission on Development and Environment for Amazonia, _Amazonia Without Myths,_ (Inter-American DeveloPment Bank and United Nations Development Programme, Washington, DC, 1992).

49. A. Grey and S. Hvalkof, _IWrIA Yearbook 1989,_ 230-243 (1990).

50. R. Simeone, _Tebiwa, The Journal of the Idaho Museum of Natural History,_ 24, 7-12 (1990).

51. P. Bunyard, The _Colombian Amazon - Policies for the Protection of its .ndigenous Peoples and their Environment_ (The Ecological Press, Cornwall, England, 1989).

52. The authors would like to thank Lynne Greabell for her bibliographic and research assistance. Also, we would like to thank Maritta Koch-Wesser, Daniel Gross and Mac Chamin for their helpful comments.
TABLE A.4 Testing for Differential Attrition

||(1)|(2)|(3)<br>Panel A: A|(4)<br>ll students|(5)|(6)|
|---|---|---|---|---|---|---|
|||_Y2_|||_Y3_||
||Treatment|Control|Diference<br>(F.E)|Treatment|Control|Diference<br>(F.E)|
|Fraction staying in the sample|0.87|0.87|-0.01|0.86|0.87|0.01|
||[0.33]|[0.34]|(0.01)|[0.35]|[0.34]|(0.01)|
|Observations|42,980|85,658||18,974|36,255||
|||Panel B:|Selection on stud|ents with _Y0_ scor|e _>_ mean||
|||_Y2_|||_Y3_||
||Treatment|Control|Diference<br>(F.E)|Treatment|Control|Diference<br>(F.E)|
|Fraction staying in the sample|0.89|0.90|-0.01|0.87|0.90|0.02<sup>_∗_</sup>|
||[0.31]|[0.30]|(0.02)|[0.33]|[0.31]|(0.01)|
|Observations|21,910|43,104||10,907|20,343||
|||Panel C:|Selection on stud|ents with _Y0_ scor|e _≤_mean||
|||_Y2_|||_Y3_||
||Treatment|Control|Diference<br>(F.E)|Treatment|Control|Diference<br>(F.E)|
|Fraction staying in the sample|0.85|0.84|0.00|0.83|0.83|0.01|
||[0.36]|[0.36]|(0.01)|[0.37]|[0.38]|(0.01)|
|Observations|21,070|42,554||8,067|15,912||

_Notes:_<sup>_∗_</sup> _p <_ 0 _._ 10,<sup>_∗∗_</sup> _p <_ 0 _._ 05,<sup>_∗∗∗_</sup> _p <_ 0 _._ 01. The table presents tests on differential attrition. Different cohorts (defined in Table A.2) stay in the sample for multiple rounds of the survey. We have attrition, but attrition rates do not differ systematically between treatment and control. Panel A combines all students, Panel B and C present results based on subsets of the data (above and below mean score on the _Y0_ baseline test). School-level clustered standard errors reported in parentheses. Within-group standard deviations reported in brackets.
unless otherwise agreed in a collective agreement or stated in company policy, workers can be dismissed without severance. This means that the strength of workers and employers positions in collective bargaining is strongly determined by labor market conditions.

In short, in Indonesia workers have the right to strike although they face the risk of loosing their jobs. this is also the case in other countries, where employers can replace striking workers temporarily or permanently. Without the expectation of intervention, employers and workers would have a more precise estimate of the potential costs and benefits of prolonging a dispute, and rational behavior typically would lead the parties to no industrial action.

While there is no need for the Ministry of Manpower to intervene in disputes, a number of disputes have arisen over matters of regulations such as dismissal procedures, or enforcement of minimum wages. Indeed, the increased involvement of the Ministry of Manpower in the determination of wages (after doubling the minimum wage between 1988 and 1995) is an invitation to intervention in dispute resolution over wage setting. When public authorities intervene in the resolution of disputes, strikes tend to become objects of political battles for or against the government. In the case of Indonesia, for example, workers can try to get a wage increase by pressuring the government to increase the minimum wage.

Moving towards a situation where conflict resolution is influenced mainly by labor market conditions requires scaling back of intervention in matters such as wage determination, and more appropriate regulation to handle dismissals.

# 5.3. Social Security Contributions

Social security contributions are currently a small proportion of labor costs. However, unless further improvements are made to ensure financial discipline, and guarantee a link between individual contributions and individual benefits, social security contributions can become a tax. The evidence from other countries is of critical interest here (see World Bank, 1994).

Payroll taxes tend to increase labor costs and to reduce real wages with a negative effect on employment. If there are weak links between social security contributions and social security benefits, contributions are transformed into a tax. Workers and employers will try to evade the tax by changing their employment status or simply by underreporting their earnings. For example, a study of the Brazilian social security system found evidence of workers staying in the informal sector as long as possible, and switching to formal employment only to meet the requirements of the pension system.

Moreover, since the liability of the pensions system remains, these problems surface in the form of public sector deficits after some time. There is evidence of very low compliance with the current Indonesian system and in the absence of change, the 24
Most overseas migrants eventually return to Kerala. International migration, which has been primarily to the countries of the Arabian Gulf, is governed by work contracts that stipulate the duration of employment—typically two years, although contracts are renewable at the employer’s discretion. In contrast, as citizens of India, Keralite migrants to other Indian states could much more easily decide to settle permanently outside Kerala. According to the KMS data, the overall number of domestic migrants who had returned to Kerala as of 2003 was, indeed, lower—approximately 42 percent of the number of overseas returnees.

Overseas remittances from international migrants played an important role in Kerala’s economy over the 1998-2003 interval: In 1998, these transfers were estimated to be around 25.5 percent of the state’s net domestic product (NDP); in 2003, they had fallen to 22 percent (Zachariah and Rajan, 2012), but this is still a notable portion of total NDP. According to estimates from the 2003 KMS data, households with one or more overseas members were receiving an average of US$1,058 per year in remittances. We know less about the magnitude of domestic remittances sent to Keralite households from migrant family members within India during this period, as the KMS data sets prior to 2014 did not record this category of financial transfer.

In the 64<sup>th</sup> wave (conducted between July 2007 and June 2008) of India’s National Sample Survey (NSS), a large nationally representative household survey, information on remittances was collected for the very first time. In 2007-2008, the average amount of annual overseas remittances received by households in Kerala with at least one member working abroad was US$1,467. In comparison, the mean amount of domestic remittances received by Keralite households with at least one member working elsewhere in India was US$641 for the same period. In absolute terms, it appears that domestic remittances per migrant household in Kerala are about half the size of international transfers.

6
Table 10: Percent of family education expenses per child<sup>+</sup> covered by subsidy (subsidy/expenses ratio), by total family expenditure quintile and level of education (public <u>schools only)</u>

|Quintile|Primary|Secondary|Tertiary|All levels|
|---|---|---|---|---|
|1st|79|58|78|75|
|2nd|66|88|301*|75|
|3rd|62|53|170*|67|
|4th|42|58|59|49|
|5th|68|56|62|66|
|All|64|64|92|67|
|N|324|143|45|512|

Source: Own calculations using the 1998 Enquête Niveau de Vie des Ménages + The above was calculated using survey information on school expenses, namely answers to questions: _“How many persons in all were covered by last school year’s school expenses?”, “Were some of these expenses borne partly or fully by any other person, your employer, the state (including student grants)?_ and _“What is the total amount of this aid?”_ * In these cases, the reported subsidy amount received exceeds the reported amount spent by the family on children’s education and some of the tertiary education quintiles the cell size is less than 5 Table 11: Percent of family education expenses per child<sup>+</sup> covered by subsidy (subsidy/expenses ratio), by total family expenditure quintile and level of education (private <u>schools only)</u>

|Quintile|Primary|Secondary|Tertiary|All levels|
|---|---|---|---|---|
|1st|115*|34|37|85|
|2nd|52|57|90|59|
|3rd|48|73|-|59|
|4th|40|44|48|44|
|5th|16|17|31|17|
|All|58|45|53|53|
|N|41|28|11|80|

Source: Own calculations using the 1998 Enquête Niveau de Vie des Ménages + The above was calculated using survey information on school expenses, namely answers to questions: _“How many persons in all were covered by last school year’s school expenses?”, “Were some of these expenses borne partly or fully by any other person, your employer, the state (including student grants)?_ and _“What is the total amount of this aid?”_ * In these cases, the reported subsidy amount received exceeds the reported amount spent by the family on children’s education; furthermore some of the tertiary education quintiles the cell size is less than 5 An alternative calculation of the subsidy/expenses ratio is presented in Tables 10 and 11 by using the information on the total household education expenses, the total subsidy received by the family for all the members going to school and number of members covered by the school 18
<mark>Blazar, D., & Kraft, M. A. (2017). Teacher and teaching effects on students’ attitudes and behaviors.</mark> _<mark>Educational evaluation and policy analysis</mark>_ <mark>,</mark> _<mark>39</mark>_ <mark>(1), 146-170.</mark> Bleidorn, W., Arslan, R. C., Denissen, J. J., Rentfrow, P. J., Gebauer, J. E., Potter, J., & Gosling, S. D. (2016). Age and gender differences in self-esteem—A cross-cultural window. _Journal of Personality and Social Psychology, 111_ (3), 396.

Botea, I. Donald A. & Rouanet L. (2021) In it to win it? Self-esteem and income-earning among couples. _Journal of Economic Behavior and Organization_ 187 (2021) 488–506 <mark>Bowles, H. R., and Flynn, F. (2010). Gender and persistence in negotiation: A dyadic perspective.</mark> _<mark>Academy of Management Journal</mark>_ <mark>,</mark> _<mark>53</mark>_ <mark>(4): 769-787.</mark> <mark>Bowles, S., Gintis, H., & Osborne, M. (2001). The determinants of earnings: A behavioral approach.</mark> _<mark>Journal of economic literature</mark>_ <mark>, 39(4), 1137-1176.</mark> Buchan, N. R., Croson, R. T. A., and Solnick, S. (2008). Trust and gender: An examination of behavior and beliefs in the investment game. _Journal of Economic Behavior & Organization, 68_ (3-4): 466– 476. https://doi.org/10.1016/j.jebo.2007.10.006 Campos, F., Frese, M., Goldstein, M., Iacovone, L., Johnson, H. C., McKenzie, D., and Mensmann, M. (2017). Teaching personal initiative beats traditional training in boosting small business in West Africa. _Science, 357_ (6357), 1287-1290.

<mark>Carneiro, P., Crawford, C., & Goodman, A. (2007). The impact of early cognitive and non-cognitive skills on later outcomes.</mark> Chakravarty, S., Das, S., and Vaillant, J. (2017). Gender and Youth Employment in Sub-Saharan Africa: A Review of Constraints and Effective Interventions. _World Bank Policy Research Working Paper_ , No. WPS 8245.

Charness, G. and Rustichini, A. (2011). Gender differences in cooperation with group membership. _Games and Economic Behavior, 72(1): 77–85_ <mark>Chioda, L., Contreras-Loya, D., Gertler, P., & Carney, D. (2021). Making Entrepreneurs: Returns to Training Youth in Hard Versus Soft Business Skills (No. w28845).</mark> _NBER Working Paper Series 28845_ Christenson, R. and Knezek, G. (2014). Comparative Measures of Grit, Tenacity and Perseverance. _International Journal of Learning, Teaching and Educational Research 8(1): 16-30._ <mark>Cobb-Clark, D. A., & Tan, M. (2011). Noncognitive skills, occupational attainment, and relative wages.</mark> _<mark>Labour Economics</mark>_ <mark>,</mark> _<mark>18</mark>_ <mark>(1), 1-13.</mark> Correll, S. (2001). Gender and the Career Choice Process: The Role of Biased Self‐Assessments. _American Journal of Sociology, 106_ (6): 1691-1730.

32
Table 7: Average Treatment Effect of Export Finance Scheme (EFS) on the Average Growth Rate of Export Outcomes

||Export<br>value|#<br>products|#<br>destinations|
|---|---|---|---|
||(1)|(2)|(3)|
|OLS|0.258***|0.023**|0.042***|
||(0.035)|(0.011)|(0.012)|
|Inverse probability|0.070***|0.007|0.018|
|(IPW)|(0.021)|(0.010)|(0.012)|
|Propensity score|0.071***|0.003|0.021|
|(PSM)|(0.025)|(0.016)|(0.016)|
|Mahalanobis matching|0.072***|0.004|0.013|
|(NNM)|(0.025)|(0.014)|(0.016)|
|Average growth rate||||
|of treated frms|-0.013|0.011|0.017|

The table reports the average treatment effect on the treated firms that participated in EFS—the estimated coefficient associated with the EFS dummy in regression 1. All the covariates used for matching are also included in the estimated regression. Number of exported products is defined at the HS 8-digit level. The sample of firms used in these estimations consists of 9,873 firms which exported in both 2015 and 2017; 769 of them utilized EFS during the same period. Standard errors in parenthesis ***, significant at the 1% level; **, significant at the 5% level; *, significant at the 10% level.

LTFF had a similar impact to EFS in terms of export performance. The estimates reported in column (1) of Table 8 show that using LTFF increased the growth rate of export sales for recipient firms by 8 to 11 percentage points relative to what it would have been had these firms not participated in the scheme. Once again, assuming that the impact of LTFF across all recipient firms was equal to the average treatment effect we estimate, the results suggest that without LTFF, Pakistan’s exports in 2017 would have been US$300 million lower than if the scheme had not been in operation. The smaller impact of LTFF compared to EFS stems from the fact that fewer exporters used the financing facility between 2015 and 2017. Similar to EFS, LTFF had no significant impact on the growth rate of the number of products exported or on the number of destinations reached by recipient firms.

Our results are consistent with the findings of Akg ̈und ̈uz et al. (2018), who find that the subsidized loan program they evaluate in Turkey increases the average growth rate of exports 22
SENEGAL’S INFRASTRUCTURE: A CONTINENTAL PERSPECTIVE

- World Bank. 2011c. ―Getting Electricity-Database.‖ www.doingbusiness.org/data/exploretopics/gettingelectricity.

# Transport

- Bofinger, Heinrich C. 2009. ―An Unsteady Course: Growth and Challenges in Africa’s Air Transport Industry.‖ AICD Background Paper 16, Africa Region, World Bank, Washington, DC.

- Bullock, Richard. 2009. ―Off Track: Sub-Saharan African Railways.‖ AICD Background Paper 17, Africa Region, World Bank, Washington, DC.

- Carruthers, Robin, Ranga Rajan Krishnamani, and Siobhan Murray. 2009. ―Improving Connectivity: Investing in Transport Infrastructure in Sub-Saharan Africa.‖ AICD Background Paper 7, Africa Region, World Bank, Washington, DC.

- Gökgür, Nilgün, and Leroy Jones. 2006. ―Impact of Privatization in Africa: Air Senegal International.‖ Boston Institute for Developing Economies (BIDE) and Investment Climate Department and Africa Private Sector and Infrastructure Department, World Bank, Washington, DC.

- Gwilliam, Ken, Vivien Foster, Rodrigo Archondo-Callao, Cecilia Briceño-Garmendia, Alberto Nogales, and Kavita Sethi. 2008. ―The Burden of Maintenance: Roads in Sub-Saharan Africa.‖ AICD Background Paper 14, Africa Region, World Bank, Washington, DC.

- Kumar, Ajay, and Fanny Barrett. 2008. ―Stuck in Traffic: Urban Transport in Africa.‖ AICD Background Paper 1, Africa Region, World Bank, Washington, DC.

- Ocean Shipping Consultants, Inc. 2009. ―Beyond the Bottlenecks: Ports in Africa.‖ AICD Background Paper 8, Africa Region, World Bank, Washington, DC.

Port Autonomme de Dakar. 2010. www.portdakar.sn/spip.php?article8.

- World Bank. 2007b. _Project Appraisal Document on a Proposed Credit in the Amount of SDR59.5 Million ($90 Million Equivalent) to the Republic of Mali for a Second Transport Sector Project._ Washington, DC: World Bank.

- ———. 2008. _Implementation Completion and Results Report (IDA-31830 IDA-3183A) on a Credit in the Amount of 64.3 SDR Million ($90 Million Equivalent) to the Republic of Senegal for a Second Transport Sector Project_ . Washington, DC: World Bank.

- ———. 2010b. _Project Information Document, Concept Stage_ . Report AB3075, World Bank, Washington, DC, May 27.

- ———. 2010c. _Project Appraisal Document Transport and Urban Mobility Project_ . Report 53774-SN, World Bank, Washington, DC, May 3.

- ———. 2010d. ―International LPI: ranking‖.

www.worldbank.org/PREM/LPI/tradesurvey/mode1b.asp#ranking]

- ———. 2010b. ―Senegal Estimates for the Use of Improved Sanitation Facilities.‖

www.wssinfo.org/resources/documents.html.

48
**Table 6:  Export destinations (2015, percentages)**

||Ma<br>|nufacturing<br>||A<br>|griculture<br>||
|---|---|---|---|---|---|---|
||Intermediate<br>inputs (II)|Final<br>demand<br>(FD)|Ratio<br>(II/FD)|Intermediate<br>inputs (II)|Final<br>demand<br>(FD)|Ratio<br>(II/FD)|
|Rest of the world|.267|.081|3.3|.228|.097|2.4|
|European Union|.203|.084|2.4|.383|.106|3.6|
|Neighbors|.096|.036|2.7|.031|.008|3.9|
|Other SSA countries|.049|.049|1.0|.019|.030|0.6|
|United States|.033|.025|1.3|.015|.006|2.5|
|Other African countries|.028|.021|1.3|.022|.008|2.8|
|China|.024|.003|8.0|.041|.005|8.2|

Notes: Population weighted weighted averages across 40 of the 45 countries (without ETH, SDS, SUD, TZA, ZWE out of precaution for potential data problems).

In the last two columns of Table 6 we show the corresponding statistics for agricultural exports. The top two destinations are the same, and they even account for 80% of all exports (compared to two thirds in the case of manufacturing). For most export destinations, the relative importance of intermediate input exports is (relatively) higher for agriculture than for manufacturing output. The greater importance of intermediate goods exports, accounting for 74% in agriculture against only 70% in manufacturing, indicates that these products are still processed abroad before reaching final consumers. An often-suggested way to increase the manufacturing sector in sub-Saharan African region would be to expand processing activities domestically.

Finally, in Table 7 we report the export intensity for all broad industry groupings that we created.<sup>19</sup> Tables A.3 and A.4 in the Appendix contains the detailed country-destination breakdown for manufacturing, but it is useful to put those statistics in perspective. According to the Eora data, the average share of manufacturing in GDP is only 12% across the subSaharan countries (population weighted). The most important sector is business services, which also includes transportation, communication, and finance.

Naturally, the share of agriculture would be a lot higher if shares were calculated in terms of employment. The very low average share in GDP of 5.4% for agriculture suggests that it excludes agricultural output for own consumption in the household. We have not manipulated these shares, but report them as they appear in the Eora dataset. It is important to realize that all work with Eora assigns this low a percentage to the agriculture sector. The unweighted average across countries is slightly higher, but still only 6.4%.

The first four entries in Table 7 are the goods-producing industries, and they have the highest export shares. It is notable, however, that exports are not zero for the other sectors. Given the large overall size of the three service sectors listed in the bottom rows, their contribution to aggregate exports are not negligible. The average export share for manufacturing is 8%, as indicated already in Table 4. Compared to the other goods-producing sectors, its exports contain much more final products.

> 19 The sector “re-export and re-import” is omitted from the table as it accounts for less than 0.1% of GDP.

28
<!-- Start of picture text --> Moldova: Prices and Lei/USD exchange rate<br>Soo - _ __ _ _ __ _ _ 13<br>450 12<br>400 12<br>35s)<br>350 / ,t' '10<br>300<br>250 -<br>200 - -<br>O - - - - - --- - - - - - - - - - - - - - 7<br>lo: ~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~6<br>so 5<br>0 . . .4<br>- - Consumet Pnce Index (CPI), Dec-94-100 Total CPI - - - Wholesale Price Index (WPI), Dec-94-100 Exchange rate, LeL/USS<br>Source Moldova Economic Trends (2001)<br><!-- End of picture text -->

**Figure** 3

This paper exploits the observed effects of the Russian crisis on the Moldovan economy to identify the vulnerability in consumption, health and education among households. The next section discusses those dimensions of development and their methodological approach.

# **3. Measures of welfare and vulnerability**

This paper uses the definition of vulnerability as "the propensity to suffer a significant fall in welfare." The operational representation involves two different dimensions. First, the measure of welfare requires narrowing the welfare dimensions to a few tractable ones. While most of the analyses have focused on monetary consumption (and/or income) vulnerability, this paper examines --in addition-- two other dimensions related to human capital investments: health and education.

The second issue is how to operationalize the "propensity to suffer significant losses in welfare." A strand of the emerging literature has defined as ex-ante poverty risk. According to this view, a vulnerable household is defined as that for which the probability of having consumption below the poverty line is greater than some probability threshold. This threshold could be any value, but typically that of one half or the estimated poverty incidence is used (Chaudhuri, 2000; Chaudhuri, Jalan and Suryahadi, 2001; Dercon, Pritchett, Suryahadi and Sumarto, 2000). In most of these studies predicted outcome distributions rely on the assumptions about the random errors. Shocks are subsumed in the errors and all the information on risk (predicted probabilities) comes 6