rafmacalaba/datause-encoder-data huggingface dataset:
# _5.4 Impacts on Learning Outcomes_

Table 9 measures impacts of the two different types of cash transfers on learning. We examine scores on a standardized mathematics and French test (French is the official language in Burkina Faso and the language used in all primary schools), which were designed by the survey team in collaboration with education specialists at the Burkina Faso Ministry of Education. The tests were given to all children ages 7 to 15 in the surveyed households. We examine the impacts separately for enrolled children (as indicated by the school data) and for all children. We also examine impacts on final end-of-year school grades (column 1). Unlike the tests we designed for French and mathematics, final grades are only available for enrolled children. They are also not standardized and can thus vary across schools or even within schools. In columns 2 and 5, the dependent variable is the age-standardized z-score for the number correct on the mathematics test. In columns 3, 4, 6, and 7, the dependent variables are age-standardized z-scores for the number correct on the overall French test and the French reading sub-section, respectively.

We find no significant impact of the interventions on grades or achievement tests, except for a positive impact of CCTs on the reading section of the French test for the sample of all children.<sup>30</sup> However, it is important to stress that even though there is no differential learning across treatment and control groups, this does not mean there is no learning going on for these children. For children in the treatment groups who get enrolled between baseline and round 3, their mean test scores increase and they improve at the same rate as for children in the control group who get enrolled across rounds. Our findings imply that transfers increase enrollment, and these children (who would not have enrolled absent the intervention) are learning as much as their peers in the control group. This can further be seen by comparing the results for all children with only enrolled children. For all children, the coefficients tend to be positive (especially for

> 30 Results (not shown) using the two alternative empirical specifications lead to similar conclusions.

23
<!-- Start of picture text --> Fig. 1c: Urban shares in unorganized sector<br>0.80<br>0.70 Unorganized Output<br>0.60<br>0.50<br>0.40 Unorganized Employment<br>0.30<br>Unorganized Plants<br>0.20<br>0.10<br>0.00<br>1989 1994 2000 2005<br>Urban shares<br><!-- End of picture text -->
housing and food consumption. These are the categories for which prices increased the most in the ECA region in 2022.

We can analyze the sources of the differences between the COL and CPI inflations using decomposition (5). The impact of a particular consumption component on that difference is determined by the absolute difference in the share of that component in the COL and CPI consumption budget and the magnitude of the price change for that component. If the share of a component is small or the differences in the shares between two baskets are small, even significant price changes will have a negligible impact on the difference between COL and CPI inflations. <u>Table A2</u> in the Appendix presents the decomposition of the COL and CPI differences by 12 consumption components separately for each country in our sample.

For the majority of the countries, differences in COL and CPI inflations are influenced by the differences in the share of food between the COL and CPI baskets, and by the high level of food inflation. The effects of other components are more heterogeneous across the countries. Surprisingly, energy prices (included in the housing component), while contributing to the increasing gap between the two inflations in Albania, Belarus, Moldova, and Uzbekistan, reduced that gap in Turkey, Poland, and Montenegro. Similarly, the transport category widened that gap in Armenia, Bulgaria, Georgia, Croatia, Serbia, and Turkey and narrowed the gap in Bosnia and Herzegovina, Belarus, Moldova, and Uzbekistan.

# **7 Implications of using COL and CPI to estimate poverty and inequality**

To understand the economic significance of using COL inflation instead of standard CPI inflation, we simulate the poverty and inequality rates using COL inflation and compare them with rates calculated based on CPI inflation.

<u>Figure 10</u> shows the simulated poverty headcount and poverty gap rates at the $6.85 (PPP 2017) poverty line for the 19 EMDE ECA countries (Foster et al. 1984). Panel (a) of <u>Figure 10</u> demonstrates that COL-based poverty is consistently higher than CPI-based poverty for virtually all countries in the region, and this difference widened during periods of high inflation in 2022 (arrows pointing north-northeast in panel a). Absent changes in nominal income, using the COL inflation rates rather than the standard CPI in 2022 would yield a 1 percentage point higher poverty headcount rate for the average country in EMDE ECA. The same difference in 2021 was, on 16
**Table 5.7  Enterprises Engaged in International Trade ***

||**July 1998**||**May 2000**||
|---|---|---|---|---|
||**Number**|**Share**|**Number**|**Share**|
|**_Total Enterprises_**|**_5100_**|**_100%_**|**_11059_**|**_100%_**|
|<br>State Owned|1938|38%|2544|23%|
|Foreign Invested|1377|27%|1548|14%|
|<br>Domestic Non-state|1785|35%|6967|63%|

Source: General Department of Customs, 2001 - * Counts of firms engaged in trade are only made at certain times during the year.

58. Total export revenues in the domestic non-state sector have also grown much faster than SOEs in the past few years. Between 1998 and 2000, exports by domestic non-state companies more than doubled, while exports by SOEs grew only 4.5 percent. More than 90 percent of export growth appears to have been due to private enterprises and more than 40 percent entirely to the domestic private sector.

**Table 5.8 Non-oil Export Growth by Enterprise Type**

||**1998**<br>**(USD Mn)**|**2000**<br>**(USD Mn)**|**2 Year Export**<br>**Growth(%)**|**Share in Export**<br>**Growth(%)**|
|---|---|---|---|---|
|State Owned Enterprises|5,073|5,255|4.7|8.5|
|Foreign Invested Enterprises|1,983|3,308|67.8|47.7|
|Domestic Non-State Enterprises|1,072|2,384|115.1|43.8|
|**Total**|**8,128**|**10,947**|**34.7**|**100.0**|

Source: General Department of Customs and Ministry of Trade 2000, World Bank, 2001 59. Private sector output in labor-intensive manufacturing industries is growing strongly. Between 1995 and 1999, the formal private sector contributed nearly 15 percent to total manufacturing growth, quite a substantial contribution given the relative size of the sector. The share of private manufacturing output in total output increased 25 percent (from 8% to 10.3%). Manufacturing output per worker in 1999 stood at 44 Million VND.

28
design, by comparing households which were surveyed immediately before the ITN campaign to those surveyed immediately after the campaign. This provides a unique opportunity to study the reactions of citizens, in real time and in a nationally representative sample, to a large scale public health campaign that involved direct distribution of well-known and valued items to individuals.

I find that citizens increase their approval of local politicians and government officials significantly (village chairmen, ward councilors, ward executive officers, and MPs) as a result of the 2010 net distribution campaign. Effects are notably stronger in districts which have higher underlying rates of malaria endemicity. The magnitude of effects fades sharply over time, but remain of considerable magnitude as long as one year after the program.

These findings are among the first to empirically demonstrate that citizens in developing countries change their opinions about political leaders in response to specific health programs. While changes in public support have been demonstrated (as discussed below) in some cases for cash transfer programs in Latin America and Southeast Asia, similar dynamics have not been shown via experimental or quasi-experimental design in sub-Saharan Africa, or for distribution of a health product. Moreover, the bed nets studied here are worth just a few dollars, which is just a small fraction of beneficiaries’ income, in contrast to the 25 to 50 percent of income that CCT programs often represent (Labonne, 2013; Manacorda, Miguel and Vigorito, 2011).

Second, in contrast to other contributions in the literature, these findings enable a clearer isolation of voter response to government actions themselves, rather than the economic benefits that governments actions can produce. The emerging literature on conditional cash transfers has an important weakness: because cash transfers represent both a program delivered by government (which could produce a rational decision to credit the government) and an immediate increase in income and living standards (which has been shown to increase approval of politicians even when they are not directly responsible for the economic growth), these studies cannot credibly isolate the mechanism at work. In other words, standard “pocketbook voting” cannot be separated from voter evaluation of a specific government benefit such as the CCT. This paper enables clearer isolation 4
the following statement (translated into English here): _”281,000 irregular Venezuelan migrants have been legalized through the Permiso Especial de Permanencia, which grants them a work permit, access to social programs (such as a subsidized health regime), and access to financial services.”_ Figure F.1 illustrates how the information was presented. The survey was executed between October and December of 2022. The sample size was 1,040 individuals and included Colombian residents who were older than 21 years old and living in Bogot ́a.

The survey followed a structured sequence. First, respondents provided demographic and socioeconomic information, including age, sex, marital status, economic stratum, occupation, formal employment status, education level, student status, and news consumption related to national politics. Second, we included the social desirability bias scale from Crowne and Marlowe (1960) to standardize and control for potential social desirability effects. Third, participants took part in the experiment. They were given five minutes to read informational flyers without any verbal explanation from the enumerators. Both control and treatment groups received flyers with general migration statistics. However, the treatment group also received additional information about the PEP program, detailing work permits, social benefits, and access to financial services.

After reading, participants answered questions on voting intentions, social capital, and attitudes toward Venezuelan migrants. The final section of the survey assessed knowledge of the PEP program. Respondents who reported familiarity with the PEP were asked: (i) Who is eligible for the PEP? (ii) How many Venezuelans currently hold the PEP? (iii) What are the benefits of the PEP? (iv) What rights does the PEP grant to migrants?

Those who stated they were unfamiliar with the PEP were instead asked about the Estatuto Temporal de Protecci ́on (PTT), the most recent regularization policy for Venezuelan migrants. If respondents were unaware of both the PEP and PPT, they were asked: (i) Can Venezuelan migrants regularize their status in Colombia? (ii) What rights do regu33
8 _Table 2: Percent Change in Health Indicators by Provinces, 1983-93 (Provinces ranked by per capita income)_

||||_P_|_ercent Chan_|_ge_|||
|---|---|---|---|---|---|---|---|
|_Quintile_<br>_of_||_Public_<br>_Health_<br>_Spending_|_Infant_<br>_Mortality_<br>_Rate_|_Maternal_<br>_Mortality_<br>_Rate_|__Covera_<br>_DPT3_|_ge_<br>_of_<br>_Sanitation_|_Safewater_|
|_Provinces_||||||||
|Poorest|I|93|-4.1|-4.3|68.8|58.6|89.1|
||II|77|-21.8|-4.4|58.9|85.4|90.2|
||III|69|-20.3|-10.7|50.2|77.4|59.9|
||IV|65|-30.1|-17.3|67.1|77.3|73.1|
|Richest|V|58|-32.5|-18.3|57.3|38.2|35.4|

**_Source:_** Staff Estimates from MOH data.

# **Costs and Quality:**

The average private out-of-pocket costs per visit to public health services vary widely across quintiles. Private expenditures on health care are shown in Table 3, ranked according to quintiles, from poor to rich in urban areas. Although the wealthiest households pay larger absolute amounts on medical items, as a proportion to non-food household spending, the poorest households spend about 1.5 times more on medical care than do richer households. Of total non-food expenditure, drugs and medicine consume about 3 times more among the poorest quintile than among the top quintile. User fees-non-food expenditure ratio--a measure of affordability--is much larger for the poorest 20 percent (2%) than for the richest 20 percent (0.60%). The top quintiles spend more on other category which mainly comprises charges for physician's home visits.

One indirect indication of quality gap is the difference in private expenditures on medical care across quintiles of household income for the same level of services. According to the findings in Table 3 the top quintile pay more in absolute amounts and buy better quality, particularly instruments and drugs. Another indicator is the quality of medical attention received by the lowincome population who use public health facilities. The 1993 NHS suggests that the poor are more likely to use Township medical centers when sick, but the probability of being treated by qualified doctor is less than 20 percent. Whereas, the rich are more likely to seek treatment in hospitals where the probability of consulting trained doctor is more than 60 percent. A third indicator is the paucity of qualified medical personnel in poorer provinces which exacerbates the quality of medical attention. The calculation of relative medical personnel ratio (RPR) in Table 4 shows a clear undersupply of qualified doctors in poorer provinces and an oversupply of such personnel in richer provinces.
# Table 37: EARNINGS FUNCTION BY YEARS OF GENERAL EDUCATION

||||G|eneral E|ducation||||
|---|---|---|---|---|---|---|---|---|
||0-4 y|ears|5-10|years|> 10|years|> 6|years|
|Intercept|9.98|(42.6)|10.10|(60.8)|10.05|(14.6)|9.32|(20.3)|
|Years of Primary Schooling|.028|(0.3)|<br>_-||-|_|-||
|Years of Secondary Schooling|-|_|.133|(4.1)|<br>.211|(2.2)|.150|(5.1)|
|Yea.s of Higher Schooling|_<br>-||-_||.143|(3.7)|.151|(4.2)|
|Years of Current Job Experience<br>|.054|(2.1)|<br>.121|(7.3)|<br>.128|(3.3)|<br>.110|(5.8)|
|(Years of Current Job Experience)<br>2|-.0010|<br>(1.2)|-.0023|<br>(4.0)|-.0031|<br>(1.7)|-.0022|(3.1)|
|Years of Previous Job Experience<br>|.024|(0.5)|<br>.040|(0.9)|<br>.242|(3.2)|<br>.098|(2.1)|
|(Years of Previous Job Experience)<br>2|-.0002|<br>(0.1)|-.0002|<br>(0.1)|-.020|(2.9)|-.0053|(1.4)|
|Years of Apprenticeship|.047|(1.2)|<br>.006|(0.2)|-.673|(2.1)|-.253|<br>(3.3)|
|Years of Secondary VTE|-|-|.082|(2.7)|<br>.002|(0.1)|.054|(2.0)|
|Years of Post-Secondary VTE|-|-|-|_|.155|(1.3)|.031|(0.3)|
|Years of Teacher Training|-|-|.060|(0.5)|<br>.090|(0.8)|.077|(1.0)|
|CEPE|-|-|.102|(0.7)|<br>-|_|.975|(2.1)|
|BEPC|-|-|.073|(0.3)|<br>-.212|(0.5)|.819|(1.7)|
|Secondary VTE Diploma|-||.212|(1.2)|-.024|(0.1)|1.10|(2.4)|
|Post-Secondary VTE Diploma|_<br>-||-|-|-.059|(0.1)|1.42|(2.6)|
|Teacher Diploma|-|_|.192|(0.6)|<br>-.052|(0.1)|1.13|(2.3)|
|Other Diploma|-|_|.500|(1.4)|<br>-.150|(0.3)|1.06|(2.2)|
|Sex (1-female)|-.118|(0.5)|-.303|(2.9)|-.053|(0.3)|-.171|<br>(1.7)|
|Non-Ivorian|-.335|(2.1)|-.247|(1.5)|<br>.337|(1.1)|<br>.178|(1.0)|
|Seasonal Job|-.446|(1.4)|<br>-.807|(2.3)|<br>-|-|-.130|(0.2)|
|Abidjan|.431|(2.4)|-.076|(0.8)|-.166|(1.2)|-.154|<br>(1.7)|
|Rural Areas|-.273|(1.1)|-.100|(0.6)|-.051|(1.6)|-.141|<br>(0.8)|
|R2|.25||.|55|.5|5|.|59|
|F|4.31||14.|80|5.9||16.|0|
|n|151||23|8|11|1|25|8|

Note: Absolute t - values in parentheses.
- 9 -

relatively healthy current account position of Turkey therefore relies on unrecorded flows, which may pose problems in the future.

Table 6: **Allocation of Capital Account, US** $ **Billions**

||Capital<br>Account<br>Balance|Capital Account incl<br>Net<br>Errors and<br>Omissions|<br>Reserve<br>Accumulation|Current<br>Account<br>Balance|Net Resource<br>Balance Deficit|Net Factor<br>Payments<br>and Transfers|
|---|---|---|---|---|---|---|
|1986|2.1|2.3|0.8|-1.5|3.1|-1.6|
|1987|1.9|1.8|1.0|-0.8|3.2|-2.4|
|1988|-1.0|-0.7|0.9|1.6|1.8|-3.4|
|1989|0.8|1.8|2.8|1.0|4.2|-5.2|
|1990|4.0|3.9|1.3|-2.6|9.6|-6.9|
|1991|-2.4|-1.3|-1.0|0.2|7.3|-7.6|
|1992|3.6|2.5|1.5|-1.0|8.2|-7.2|
|1993|9.0|6.7|0.3|-6.4|14.2|-7.7|
|1994|-4.2|-2.4|0.2|2.6|4.2|-6.8|
|1995|4.6|7.0|4.7|-2.3|13.2|-10.9|
|1996|8.8|7.0|4.6|-2.4|10.6|-8.2|
|1997|8.6|6.1|3.3|-2.8|15.5|-12.7|

Table **7 Allocation of Capital Account,** % of Total

|Re|serve Accumulation|<br>Current Account|Net Factor Payments|Net Resource Balance|
|---|---|---|---|---|
||||and Transfers|Deficit|
|1986|35|65|-72|137|
|1987|55|45|-137|182|
|1988|-126|226|478|-252|
|1989|153|-53|-288|234|
|1990|33|67|-176|243|
|1991|80|20|593|-574|
|1992|60|40|-294|333|
|1993|5|95|-115|210|
|1994|-8|108|282|-174|
|1995|67|33|-155|189|
|1996|65|35|-117|152|
|1997|55|45|-209|254|

# **III. The Liberalization of the Capital Account and Determinants of Capital Flows**
# **References**

Banerjee, A. and Newman, A. (1993) Occupational choice and the process of development. _Journal of Political Economy_ , 101(2) pp. 274-298 Carter, M., Little, P., Mogues, T., and Negatu, W. (2007) Poverty trap and natural disasters in Ethiopia and Honduras. _World Development_ , 35(5), pp. 835-856 Chen, S., Mu, R. and Ravallion, M. (2009) Are there lasting impacts of aid to poor areas?. _Journal of Public Economics_ , 93(3-4), pp. 512–528.

Corden, W. (1984) Booming sector and Dutch disease economics: survey and consolidation _. Oxford Economic Papers_ , 36(3), pp. 359-380 de Mel, S., McKenzie, D. and Woodruff, C. (2008) Returns to capital in microenterprises: Evidence from a field experiment. _Quarterly Journal of Economics_ , 123(4), pp. 1329–1372.

Dercon, S. (2002) Income risk, coping strategies, and safety nets. _World Bank Research Observer_ , 17(2), pp. 141–166.

Fafchamps, M., McKenzie, D., Quinn, S., and Woodruff, C (2011) When is capital enough to get female microenterprises growing?: evidence from a randomized experiment in Ghana. _NBER Working Paper #17207_

> FAO (2005) Fishing vessel quality issues [Report]. Retrieved from <u>ftp://ftp.fao.org/ Fi/DOCUMENT/ tsunamis_05/indonesia/livelihoods/BoatbuildingBookEnglish.pdf</u> Field, E. (2007) Entitled to work: urban property rights and the labor supply in Peru. _Quarterly Journal of Economics_ , vol. 122, no. 4, pp. 1561-1602

Foster, A. and Rosenzweig, M (2004) Agricultural productivity growth, rural economic diversity, and economic reforms: India, 1970-2000. _Economic Development and Cultural Change_ , 52(3), pp. 509-542 Frankenberg, E., Gillespie, T., Preston, S., Sikoki, B., and Thomas, D. (2011) Mortality, the family and the Indian Ocean tsunami. _Economic Journal_ , 121(554), pp. 162-182 Galor, O. and Zeira, J. (1993) Income distribution and macroeconomics. _Review of Economic_
# **2 A Ricardian theory of robots, tasks and trade**

We develop a multi-sector, multi-country Ricardian model with two-stage production and trade in intermediate and final goods in which robots can replace labor in a subset of industries. The model builds on Eaton and Kortum (2002), Caliendo and Parro (2015), Artuc and McLaren (2015) and Acemoglu and Restrepo (2017b). We denote countries by _m_ and _n_ and sectors by _i_ . We denote production stages by _s_ , where _s_ = 1 refers to intermediate inputs (first stage) and _s_ = 2 refers to final goods (second stage).<sup>4</sup> All markets are perfectly competitive. Workers can move freely across stages and sectors but are immobile across countries. Robots are equally available in all countries, at the same (exogenous) rental rate. They are owned by residents of the country that robotizes production.

## **2.1 Preferences**

The representative household in country _m_ maximizes utility by consuming a composite of final good varieties _Q_<sup>_m,i_</sup> 2 . Preferences are described by a Cobb-Douglas utility function, given by where _Q_<sup>_m,i_</sup> 2 is the amount of composite final good from sector _i_ demanded by consumers in country _m_ and _γ_<sup>_m,i_</sup> is a constant, with<sup>�</sup> _i_<sup>_γm,i_= 1.Thecompositefinalgood</sup><sup>_Qm,i_</sup> 2 results from the aggregation of final stage varieties by consumers, as described in detail below.

## **2.2 Varieties and production stages**

A continuum of varieties _ω ∈_ [0 _,_ 1] is produced in each sector _i_ of country _m_ . These varieties can be produced either as intermediate inputs in the first stage or as final goods in the second stage. We define the set of first and second stage varieties in industry _i_ respectively as _S_ 1<sup>_i_and</sup><sup>_S_</sup> 2<sup>_i_,such</sup> that _S_ 1<sup>_i∪S_</sup> 2<sup>_i_= [0</sup><sup>_,_1].</sup> Production of these varieties, both in the first and second stages, requires three types of inputs: a fixed factor specific to the industry-stage; a composite task input; and a composite first stage output from the same industry, _Q_<sup>_m,i_</sup> 1 . Thus, a set of tasks are performed on the composite

> 4For simplicity, we assume a two-stage production process. However, as discussed below, the key mechanisms we emphasize would also apply in a multi-stage setting. Leading trade theories with multi-stage production include Yi (2003, 2010), Antras and Chor (2013), Antras and de Gortari (2017), Fally and Hillberry (2018) and Lee and Yi (2018).

5
|Kyaukpyu Port,|Middle|Port project scaled|The port did not create any new routes that are sufficiently low in cost to|
|---|---|---|---|
|Myanmar|tercile|down from 10|generate any increases in land rents.|
|||berths to 2 berths.||

# Table References

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Iran Ministry of Roads and Urban Development (2018). Tehran-Mashhad Electrified Railway Studies Finalized.

CPEC Secretariat (2018). Expansion and reconstruction of existing Line ML-1. CPEC.gov.pk.

CSIS Reconnecting Asia (2018). Kuantan Port: New Deep Water Terminal Phase I.

Hunt, Luke (2017). Construction of the Thailand-China Railway Finally Gets Underway. The Diplomat.

Financial Times (2019). Malaysia renegotiated China-backed rail project to avoid $5bn fee.

Kapoor, Kanupriya and Aye Min Thant (2018). Exclusive: Myanmar scales back Chinese-backed port project due to debt fears – official. Reuters.

46