rafmacalaba/datause-encoder-data huggingface dataset:
12 UNIDO and GATT(2).'<sup>4</sup> For example, under the IBRDIUNCTAD/GATT definition the 72 developing countries are estimated to export some $224 billion in manufactures (or 32 percent of total exports) while under the UNIDO definition (which is consistent with that used for output series) manufactured exports total $277 billion (50 percent of total exports on average). In other words, under IBRD/UNCTAD/GATT the value of developing countries' manufactured exports are 24 percent below those reported using the UNEDO dernition.'<sup>5</sup> However, the discrepancy widens (to $320 billion as opposed to $224 billion ) when the IBRD/UNCTAD/GATTtradedefuiitioniscomparedwiththeGATT(2)productiondefinit;onofmanufactured products.

While the differences are impressive in the aggregate, some of the individual country resuts are especially striking. Inclusion of refmed petroleiim, processed foods and consumable alcoholic beverages in the UNIDO definition causes the share of Algeria's manufactured exports to rise 32 percentage points (34.5 percent), while the corresponding share for Argentina more than doubles (to 68.9 percent). As another measure of the importance of the difference in definitions, Table 2 shows 55 of the countries (76 percent of the total) report manufactures exports at least 25 percent greater under the UNIDO definition, while the 14 The World Bank (WD&87 and UNIDO decision to drop pearls and precious stones (SITC 667) from manufactures is a key factor accounting for some major discrepanoies in results for the African countries. For example, under IBRDIUNCTAD/GATT Gambia and Sierra Leone reportedly export **_$85_** million of manufactures, but when precious stones are deleted about $1 million are reported as manufactured exports. Since the African countries exports consists almost entirely of uncut stones the deletion appears appropriate. However, dropping SITC 667 causes Israel's manufactured exports to fall by about 40 percent. In this case the deletion seems questionable since Israel's exports consist largely of cut and worked diamonds.

''The alternative trade definitions produce less extreme, but important differences from the $224 billion in manufactures exports associated with the UNCTAD/GAT1I1BRD definition. For example, the _WM_ (87) definition produces a total of $208 billion, a 7 percent difference; UNCTAD (2) produoes a $219 billion total (2 percent difference); while the test proposal which excludes only unworked SITC 68 products produces a $225 billion total which is less than one half of a percentage point difference from UNCTAD/GATT/IBRD.
Another explanation for the vanishing effect is the view that certain financial activities crowd out more productive ones and result in a misallocation of talent in the economy that is detrimental to growth. Cecchetti and Kharroubi (2012) shows that the non-monoticity in the relationship between finance and growth is particularly strong when financial sector employment is used as a proxy for financial depth. Cecchetti and Kharroubi (2015) proposes a model to understand the source of the negative relationship between the rate of growth of the financial sector and productivity growth at high levels of financial development. The model is centered on two externalities, an investment externality through which finance favors projects with high collateral even when they are less productive, and a labor externality by which the financial sector attracts skilled labor and grows at the expense of the real economy. Using a cross-country panel of industry-level data, the paper finds supportive evidence for the two externalities.

What role do financial regulation and supervision play in controlling the risk that financial deepening results in misallocation of resources and talent? Philippon and Resheff (2012) find a tight link between the wage premium commanded by the financial industry and an index of financial deregulation. By 2006, the excess wage premium in the financial industry was about 50% in absolute terms and between 20% and 30% in certainty-equivalent terms. The premium for CEOs in the financial sector was about 250% compared to CEOs elsewhere. Interestingly only one-fifth of this premium could be related to the size of assets under management, a standard explanation for the rise in CEOs pay (Gabaix and Landier, 2008). A fraction of the rest can be due to innovation in the financial sector but might also be related to risk-taking behavior, facilitated by deregulation such as the 1999 repeal of the Glass-Steagall Act. Philippon and Resheff (2013) finds cross-country evidence on the positive link between the share of the finance industry, wage premia in the financial sector, and measures of financial deregulations. Philippon and Resheff (2012) also suggest that wage premia in the financial sector render more difficult the enforcement of financial regulations, since such activities require similar skills as those sought after in the private financial sectors but command salaries that are constrained by the public sector pay scale.

From a policy perspective, how shall we deal with "too much finance"? Tightening financial regulation should be balanced to prevent misallocation and abuse while also keeping the 16
Table 15: Robustness checks

|Variables|Specification|HWWS|TS|SM|HWWS|+ TSSM|Observations|R-squared|Control Mean|
|---|---|---|---|---|---|---|---|---|---|
|Diarrhea (past<br>|Original<br>|-0.013 (0.250)|-0.010|(0.373)|-0.021*|(0.096)|34,045|0.051|0.168|
|<br>14 days)<br>(i)|Interviewer dummies<br>|-0.016 (0.115)|-0.008|(0.421)|-0.023**|(0.024)|34,045|0.093|0.168|
||Month dummies|-0.014 (0.210)|-0.007|(0.498)|-0.019*|(0.086)|34,045|0.094|0.168|
||Original|0.030 (0.057)|-0.006|(0.059)|-0.008|(0.057)|5,208|0.084|-1.946|
|Height-for-<br>(ii)|Removing outliers|0.031 (0.054)|0.002|(0.053)|-0.004|(0.052)|5,147|0.085|-1.953|
|age<br>|Interviewer dummies|0.035 (0.054)|-0.008|(0.054)|-0.020|(0.055)|5,208|0.097|-1.946|
||Month dummies|0.034 (0.054)|-0.008|(0.053)|-0.021|(0.052)|5,208|0.100|-1.946|
||Original|0.015 (0.043)|-0.044|(0.035)|-0.075**|(0.038)|5,203|0.062|-1.033|
|Weight-for-<br>(iii)|Removing outliers|0.024 (0.040)|-0.042|(0.033)|-0.073**|(0.036)|5,170|0.063|-1.017|
|age<br>|Interviewer dummies|0.031 (0.042)|-0.050|(0.033)|-0.079**|(0.037)|5,203|0.068|-1.033|
||Month dummies|0.040 (0.040)|-0.047|(0.031)|-0.081**|(0.035)|5,203|0.071|-1.033|
|Head|Original|0.229 (0.141)|0.092|(0.129)|0.227|(0.154)|5,208|0.223|-0.511|
|<br>circumference<br>(iv)|Removing outliers<br>-|0.229* (0.123)|0.086|(0.107)|0.181|(0.131)|5,134|0.187|-0.543|
|for-age<br>|Interviewer dummies<br>|0.100 (0.095)|0.075|(0.095)|0.222**|(0.107)|5,208|0.331|-0.511|
||Month dummies|0.067 (0.100)|0.041|(0.093)|0.149|(0.106)|5,208|0.343|-0.511|
||Original|0.089 (0.754)|-0.772|(0.713)|-1.652**|(0.772)|5,203|0.194|111.441|
|Hemoglobin<br>(v)|Removing outliers|0.199 (0.714)|-0.453|(0.690)|-1.101|(0.733)|5,162|0.196|111.519|
|level (g/L)<br>|Interviewer dummies|-0.181 (0.796)|-0.916|(0.724)|-1.608**|(0.760)|5,203|0.200|111.441|
||Month dummies|-0.656 (0.803)|-1.340*|(0.723)|-2.217***|(0.785)|5,203|0.206|111.441|
|(vi)<br>Abrasions / b|ruising in past 7 days|0.001 (0.008)|0.001|(0.008)|-0.015*|(0.008)|5,764|0.042|0.044|

Notes: Robust standard errors in parentheses clustered at the ward level.*** p<0.01, ** p<0.05, * p<0.1. Coefficients estimated using a linear probability model including block fixed effects, child gender and age (month) dummies. Row (i) uses listing data; rows (ii) - (vi) use household survey data. For rows (i) - (v) outcomes are subjected to robustness tests. "Original" provides the result presented in the main tables. "Removing outliers" excludes observations that are more than three standard deviations away from the mean. "Interviewer dummies" includes control dummies for all survey interviewers. "Month dummies" specification includes both interviewer and month-of-interview dummies. Row (vi) provides evidence on the falsification test.

51
- 14 Kanyanda S, Markhof Y, Wollburg P, Zezza A. Acceptance of COVID-19 vaccines in sub-Saharan Africa: evidence from six national phone surveys. _BMJ Open_ 2021; **11** : e055159.

- 15 Solís Arce JS, Warren SS, Meriggi NF, _et al._ COVID-19 vaccine acceptance and hesitancy in low- and middle-income countries. _Nat Med_ 2021; published online July 16. DOI:10.1038/s41591-021-01454-y.

- 16 Africa CDC. COVID-19 Vaccine Perceptions: A 15 country study. 2021 https://africacdc.org/download/covid-19-vaccine-perceptions-a-15-country-study/ (accessed May 18, 2021).

- 17 Razai MS, Oakeshott P, Esmail A, Wiysonge CS, Viswanath K, Mills MC. COVID-19 vaccine hesitancy: the five Cs to tackle behavioural and sociodemographic factors. _J R Soc Med_ 2021; **114** : 295–8.

- 18 Himelein K. Weight Calculations for Panel Surveys with Subsampling and Split-off Tracking. _Stat Public Policy_ 2014; **1** : 40–5.

- 19 Ambel A, McGee K, Tsegay A. Reducing Bias in Phone Survey Samples. Effectiveness of Reweighting Techniques Using Face-to-Face Surveys as Frames in Four African Countries. Washington D.C.: World Bank, 2021 https://documents.worldbank.org/en/publication/documentsreports/documentdetail/859261622035611710/reducing-bias-in-phone-survey-samples-effectivenessof-reweighting-techniques-using-face-to-face-surveys-as-frames-in-four-african-countries.

- 20 Brubaker J, Kilic T, Wollburg P. Representativeness of individual-level data in COVID-19 phone surveys: Findings from Sub-Saharan Africa. _PLOS ONE_ 2021; **16** : e0258877.

- 21 Buguzi S. Covid-19: Counting the cost of denial in Tanzania. _BMJ_ 2021; : n1052.

- 22 Wilson SL, Wiysonge C. Social media and vaccine hesitancy. _BMJ Glob Health_ 2020; **5** : e004206.

- 23 Bursztyn L, González AL, Yanagizawa-Drott D. Misperceived Social Norms: Women Working Outside the Home in Saudi Arabia. _Am Econ Rev_ 2020; **110** : 2997–3029.

- 24 Hoy CA, Kanagavel R, Cameron CM. Intra-Household Dynamics and Attitudes toward Vaccines : Experimental and Survey Evidence from Zambia. The World Bank, 2022 https://documents.worldbank.org/en/publication/documents-

- reports/documentdetail/099239408032215339/idu00eb25fa7060d9046af09f400895c23d855f9 (accessed Aug 10, 2022).

- 25 Sallam M, Al-Sanafi M, Sallam M. A Global Map of COVID-19 Vaccine Acceptance Rates per Country: An Updated Concise Narrative Review. _J Multidiscip Healthc_ 2022; **Volume 15** : 21–45.

- 26 Lazarus JV, Ratzan SC, Palayew A, _et al._ A global survey of potential acceptance of a COVID-19 vaccine. _Nat Med_ 2021; **27** : 225–8.

- 27 Wiysonge CS, Ndwandwe D, Ryan J, _et al._ Vaccine hesitancy in the era of COVID-19: could lessons from the past help in divining the future? _Hum Vaccines Immunother_ 2022; **18** : 1–3.

- 28 Lawal L, Aminu Bello M, Murwira T, _et al._ Low coverage of COVID-19 vaccines in Africa: current evidence and the way forward. _Hum Vaccines Immunother_ 2022; **18** : 2034457.

13
Table 5.4—Availability of different types of infrastructure in student’s residence village (averaged in school areas)

|||**2000**|||**2010**|**(**|**2010–2000)**||
|---|---|---|---|---|---|---|---|---|
|Variable|TEEP|Non-TEEP||TEEP|Non-TEEP|TEEP|Non-TEEP||
|Electricity|0.865|0.838||0.988|0.972|0.123|0.134||
|Piped water|0.422|0.349||0.642|0.595|0.220|0.245||
|Paved road|0.388|0.451||0.518|0.709<br>***|0.129|0.258|*|
|Modern irrigation|0.197|0.124||0.294|0.238|0.097|0.114||
|Cell phone service|0.611|0.624||0.951|0.950|0.340|0.326||
|Internet café|0.020|0.031||0.075|0.068|0.055|0.037||
|Market|0.077|0.081||0.079|0.111|0.002|0.030|*|
|Public elementary school|0.917|0.795|***|0.891|0.804<br>**|-0.026|0.009||
|Private elementary school|0.018|0.038||0.154|0.154|0.136|0.117||
|Public high school|0.228|0.192||0.312|0.308|0.084|0.116||
|Private high school|0.040|0.032||0.123|0.121|0.083|0.089||
|Health center|0.784|0.596|***|0.766|0.619<br>**|-0.018|0.023||
|Public library|0.024|0.020||0.046|0.031|0.022|0.011||
|Post office|0.036|0.017||0.026|0.038|-0.010|0.020|**|
|Number of observations|62|39||62|39|62|39||

Source: TEEP Tracking Survey. Note: * significant at the 10% level; ** significant at the 5% level; *** significant at the 1% level.

35
Figure 16 The contribution of migration, inequality and mean expenditure to poverty reduction in Rural Areas, GLSSi and GLSS3 <!-- Start of picture text -->
-10<br>-20 -<br>Population  Inequality  Mean<br>Expenditure<br>*Formal  a Nonfarm  0 Farm<br><!-- End of picture text -->

The comparisons of GLSS1 and GLSS3, and GLSS2 and GLSS3 show that the driving force behind the poverty reduction for Other Cities and Rural Areas are the improvement in inequality in informal sector and an increase in mean expenditure per capita in informal sector for Other Cities and in farming sector for the Rural Areas. Farming seems to be more important in the improvement in the Rural Areas and the informal sector in the Other Cities. But it should be remembered that improvement in the farming group in the Rural Areas does not come necessarily or solely from an increase in agricultural output. The division of the population into the various socio-economic groups is based on the main income source of a household. In an economy in which multiple occupations of earners is the dominant mode, the increase in income could come partly from non-farm activities.

CEM pointed out that informnal employment activities cut across all sectors and essentially service-based. During 1988 to 1992, the proportion of rural self-employed working in the service sector increased from 54 to 62 percent, while the share of manufacturing employment -- of the micro- and small-scale type declined from 37 to 31 percent. Table 6 gives the changes in proportions of the different income sources in total income of the different localities. It can be seen that the share of income from non-farm activities -- or the informal sector-- increased rapidly in all localities at the expense of farming.<sup>6</sup> Thus, to conclude, there is a marked difference in the change in welfare for low income groups in Accra, on the one hand, and in the rural areas and other cities, on the other. Accra declined, but the other two registered substantial improvement. The informal sector, which expanded everywhere at the expense of the formal, contributed to

> 6The GLSS data show that while expenditure declined in Accra, household income measures increased. We tend to accept the expenditure measures as more reliable, but still think that the _shares_ of total income from different sources in the total reported would be accurate. In any event the changes in shares are large as can be seen from Table 6.

18
Host community households are those whose head of household were born in Turkey, or born outside Turkey but did not live abroad for more than a year. Conversely, migrant households are defined as those whose head of household was born abroad and has lived abroad for more than 12 months. The duration of a migrant household’s stay in Turkey is also based on when the head of household arrived in Turkey. Three thresholds are tested: 2, 3, and 4 years. The 4 year cut-off is preferred to maximize the sample size of recent migrant households.

Table 2. Population Groups for Comparison

||Group1|Group2|Group3|Group4|Group5|Group6|
|---|---|---|---|---|---|---|
|Geographic<br>Location||Near the Syrian Bord|er|T|he Rest of the Count|ry|
|Status|Host Community|Settled Migrant<br>Households|Recent Migrant<br>Households|Host<br>Community|Settled Migrant<br>Households|Recent Migrant<br>Households|
|Years in Turkey|Head of<br>Household born<br>in Turkey|Arrived in<br>Turkey more<br>than: 2, 3, or 4<br>years|Arrived in<br>Turkey less than:<br>2, 3, or 4 years|Head of<br>Household born<br>in Turkey|Arrived in<br>Turkey more<br>than: 2, 3, or 4<br>years|Arrived in<br>Turkey less than:<br>2, 3, or 4 years|

# **_Are Syrian Refugees being captured using the LFS?_**

While variables covering all topics of interest (migration, welfare, and geography) are available or can be imputed into the LFS, it may be unclear, ex-ante, if SUTPs are adequately included in the survey.

Despite a small sample of foreigners and other concerns, there is evidence that the LFS sample does include some “recent foreign” migrants, especially in the border regions (NUTS2) [TRC1-Gaziantep, Adiyaman, Kilis, TRC2-Sanliurfa, Diyarbakir, TRC3-Mardin, Batman, Sirnak, Siirt TR63-Hatay, Kahramanmaras, Osmaniye] (Map 1).

7
education SECALs (1987 and 1990) totalling $84.5 million. The first education SECAL supported major restructuring of the education system, a reorientation of expenditures to primary education, teacher training, retrenchment of nonteaching staff, a freeze on teaching posts, and reduced subsidies. The second reinforces these reforms and extends them to the secondary level.

# Ghana: educatQn indicatrs

||197|5 1980|1981|1982|1983|1984|198|5 198|6 1987 1988|
|---|---|---|---|---|---|---|---|---|---|
|Einance:<br>||||||||||
|% of public budget<br>|18.7<br>|17.2<br>|13.4<br>|20.2<br>|18.7<br>|19.1<br>|18.0<br>|23.9<br>|23.9 25.7<br>|
|% of GDP<br>|6.0<br>|3.1<br>|1.8<br>|2.1|1.0|1.0|2.6|3.3|3.3 3.5|
|real expenditures (index)|175|100|74|78||78|99|143|148 167|
|Intrasectoral finance:<br>||||||||||
|recurrent budget share_(%)_<br>|78<br>|56|||||97|||
|salary budget share (%)<br>|57<br>|||||54||74||
|primary share of budget (%)|25|29|||||27|||
|QuiMt(enrollment rates--%):<br>||||||||||
|gross primary<br>|71<br>|80<br>|77|77|78|76|76|72|71|
|net primary<br>|59|64|||||59|||
|gross secondary|37|41||38|38|41|40|39|40|
|Quality:<br>||||||||||
|per student spending (index)<br>grade 4 completion_(%)_|259<br>82|100<br>85|86|83|76|75|84|98||
|% primary students female|43|44|||||43|||

5.13 Gross primary enrollment rates declined from 1983 to 1987 because total enrollments grew at only one-third the rate of growth of the school-age population (Colclough and Lewin, 1990). The literature does not explain why total enrollment growth faltered. Perhaps enrollment rate declines are a lagged response to declining public education spending. Because declining student/teacher ratios result in part from enrollment rate declines, the former do not necessarily represent quality improvements.

5.14 Public spending per student during the 1980s remained less than half the 1975 level. From 1980 to 1985, recurrent spending per primary teacher decreased approximately 15 percent. 4,000 trained teachers left the sector between 1977 and 1981 because of inadequate salaries (Cornia, et.al., 1987). The share of the education budget allocated to teacher salaries declined from 1975 to 1984, before increasing abruptly in 1986 following a salary raise.

40
agricultural capital and skilled labor income. The long-run effects are somewhat smaller, especially for large farmer households, as investment in agricultural capital eats into their rents. Generally speaking, nominal income gains are greater for rural households.

Consumer prices increase more than nominal income for all but large farmer households, cutting real consumption. Consumer prices generally increase more for rural households under Doha, as they consume relatively more agricultural goods. This offsets the higher nominal income gains among rural households such that real consumption and welfare vary in roughly the same proportion for urban and rural households, with the exception of large farmers. More importantly for poverty analysis, consumer prices tend to rise more for the poorer household groups, due to their more intensive consumption of agricultural goods. Consequently, the reductions in real consumption and welfare are greatest for precisely the poorest household groups: landless and marginal farmers, non-agricultural rural households and urban households for which the head of household is illiterate (Table 9). In the long run real consumption and welfare changes are smaller with respect to the baseline scenario, although they follow the same pattern.<sup>8</sup> _Poverty Effects_ (Tables 9-10) _._ Foster-Greer-Thorbecke (FGT) poverty indices are used to evaluate the impacts of our simulation on the poverty profiles of the nine representative households (Foster et al., 1984).<sup>9</sup> We apply the variations in consumption for each household group from the dynamic model to generate new consumption vectors for individual households from the Bangladeshi household survey.<sup>10</sup> We use two different poverty lines for rural and urban households which are endogenously determined by the model taking into account the rural and urban CPIs. The first block of the table reports the base-case (year 2000) poverty profiles. It is evident that poverty is more acute in rural areas than in urban areas. Among rural households, poverty is most severe for landless and marginal farmers. Regarding urban households, 16
**Figure 12:** EBRD transition indicators, 5 year averages: levels and changes after removing country and period fixed effects

<!-- Start of picture text --> (a) Levels (b) Change compared to previous period<br>MNE<br>KGZ LVALTU<br>TJKMDAGEOAZEARMKAZSRB MNE ESTSRBBIHROU<br>SRB TKM MNG GEOALB<br>TKMHRVHUNESTUZBLTULVABIHTJKROUBLRRUSMNTKMSRBBGTJKMKDSVNKGZBBGR G MKD LR AZESVNRKAZCZKAZKGZPOLRUSUZBUSVKMNEARMHRVMDA EK UKRMDA A CZEMNGPOLGEOBLRALBGEOSVKARMRHUNSVKLBGEOROUPOLALBGEOROUUKRARMAZE M ARMAZEMDATKMKGZALDALTUNGCZEESTTJKCZMKDUKRAZESVKRUSUZBPOLBIHLVA B KAZKAZHUNBGRLVALVA E IHSVNMNELTUBILTUSTSVNEST H MNGRUSRVKGZROUSRBMKDMNEBGRHUNHRVUZBTJKBLR TKMTJK BLRRUS KGZMKDHUTKMSVUZBTKMUZBROU N BLRAZEMKDUZBARMPOLKGZMDAPOLALBLTUUKRAZESVKESTGEOSVNUKRBIHCZEKAZARMROUALBMDALVALVABGRMNGSVKBLRCZELTUSVKCZEESTRUSPOLHRVBGRHUNRUSUKRMNGKAZHRVBGRTJKSVNMKDHRVHUN<br>SRB BIH<br>MNE<br>MNESRB<br>−1 −.5 0 .5 1 −1 −.5 0 .5 1<br>Banking transition indicator Change in banking transition indicator<br>2<br>1<br>1<br>0<br>0<br>−1<br>−1<br>Trade and FX transition indicator −2<br>−2 Change in trade and FX transition indicator<br><!-- End of picture text --> Note: Values of the transition indicators range from 1 to 4.33 with higher values representing standards moving towards those of advanced industrial economies. Averages taken over 1989-1993, 1994-1998, 1999-2003 and 2004-2007 (four year average). Plots indicate residuals from regressions controlling for country and period fixed effects. Lines represent lowess smoother (locally weighted regression) with 95% confidence intervals.

# **B Proofs**

## **B.1 Steady state equilibrium with trade and financial autarky - Proof of existence and uniqueness**

The steady state equilibrium in home can be defined by the following system of five equations in relative prices _{w, R}_ , domestic market production cutoff _{φd}_ and aggregate quantities _{Y, B}_ from which all other endogenous variables can be derived: Household Euler equation: _R_ = 1 _/β_ Domestic production cutoff: _<u>φ</u>_<sup>_σ_</sup> _d_<sup>_−_1</sup> =<sup>_<u>w</u>_</sup> _Y_<sup>_σ_</sup> _<u>f</u>_ (( _βρ−_ Θ _ρd_ Θ)<sup>1</sup> _d_<sup>_−_</sup> )<sup>_σ_</sup> Funds market clearing: _B_ = 0 in _<u>φd</u>_ .

To prove existence and uniqueness of the equilibrium first consider the labor market clearing condition. _H_ (Θ _d,_ _<u>φd</u>_ ) is continuously differentiable (provided _g_<sup>_′_</sup> <u>(</u> _<u>φd</u>_ ) is defined) and tends to zero as _<u>φd</u> →∞_ and tends to infinity as _<u>φd</u> →_ 0. There is thus a unique value of _<u>φd</u>_ for which the labor market clearing condition holds. Substituting the equilibrium value of _<u>φd</u>_ into the aggregate pricing equation uniquely defines the real wage. The 31
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