rafmacalaba/datause-encoder-data huggingface dataset:
revenues accumulating at time _t, Rt,_ we set it to 0.95. We performed sensitivity analysis assuming different values of _Rt_ , and the results were not significantly The EU KLEMS dataset does not include information on input prices for energy and materials. We use the International Energy Agency (IEA) _Energy Prices and Taxes_ database to extract the data on end-use prices for key fuels used in the OECD manufacturing industries.<sup>23</sup> We also use the IEA _World Energy Statistics and Balances_ database to obtain industrial fuel consumption data. We then construct the average energy price for the manufacturing sector by weighting energy carriers’ prices by the consumption of each energy carrier in the manufacturing sector. To examine the robustness of our energy price calculations we calculate industrial fuel expenditure using data from the IEA databases and compare it with industrial energy expenditure reported in the EU KLEMS database. Correlation between the two series was close to 0.99 for the manufacturing sector aggregate. Unfortunately, these correlations were considerably lower for individual industrial sectors, which indicates poorer correspondence between IEA and EU KLEMS at less aggregate levels. We therefore employ energy price data for the manufacturing sector aggregate.

We construct the price of materials by weighting international commodity prices (from the IMF International Financial Statistics database) by sector consumption of each commodity (from the UNIDO Industrial production database). The data series for labor, energy, and material costs, and for the values of output and capital stock, are all deflated to their real values based on the industry deflators, using 1995 as the base year, and converted into U.S. dollars at nominal exchange rates.<sup>24</sup> Figure 1 shows the average energy prices in the manufacturing sector across OECD countries in 2005. The highest energy prices are in Italy, Ireland, Japan and Sweden, and the lowest are in Australia, the Netherlands, Greece, and the United States. These differences in energy prices across OECD countries are because of variation in energy taxes, the types of fuels used in the production process, and local distribution costs. Variation in industrial energy prices across the manufacturing industry in the OECD is relatively small compared to other sectors, such as commerce or transportation (International Energy Agency

> 23Specifically, we consider the following energy products - oil and petroleum products (highand low-sulphur fuel oil, light fuel oil, automotive diesel, and gasoline), natural gas, coal, and electricity. Consumption of each product is measured in British thermal units (BTUs).

> 24We have also estimated the model using dollar conversion at purchasing power parity exchange rates. The results were of comparable magnitude to those reported below.

17
15 Our data do not permit us to move beyond speculation. But, one conjecture, at least, is consistent with the data. Because the distributional data change with less frequency than the poverty and income data, the migration and remittance variables may be picking up the effect of a progressive bias in the distribution of remittance income among households. In this case, while the main channel by which remittances reduce poverty is via the income variable, their distributional impact is captured by the independent migration/remittances variable. The fact that in the case of the poverty gap and the squared poverty gap the elasticity of the measure with respect to remittances is greater than for the headcount may lead some credence to this hypothesis.

# 4.  Determinants of International Migration and Remittances

Since international migration and remittances reduce poverty in our full sample of developing countries, it is useful to explore the determinants of migration. In the literature the determinants of international migration are often analyzed using the type of gravity model suggested by M. Greenwood and George Borjas.<sup>23</sup> In general terms, such a model can be expressed as: _M_ ij = α0 + α1 _p_ i + α2 _y_ i + α3 cij + εij ( _i_ = 1, . ., N; _j_ = 1, . ., N) (2) Where _M_ ij is the migration flow between labor-exporting country i and labor-receiving region j,<sup>24</sup> _p_ i is the population of labor-exporting country i, _y_ i is the per capita income of labor-exporting country i, cij is the costs of migrating from country i to j, and ε is an error term.

Unfortunately, equation (2) cannot be estimated because our data set contains no information on the costs of migration (cij). Since this problem is also common to other
**Table 4 – Multiple Correspondence Analysis (Preliminary weights, Urban and Rural areas)**

||||**Urban**|**Rural**|
|---|---|---|---|---|
|**Area**|**Variable**|**Modality**|**Dim1**|**Dim1**|
|Employment|Head of household unemployed|yes|-0.4|1.21|
|||no|0.01<br>|-0.02<br>|
||Head of household employed|yes|-0.19|-0.15|
|||no|0.35<br>|0.73<br>|
||Youth unemployment|active|-0.69|-0.29|
|||unemployed|0.15<br>|1.37<br>|
|||inactive|0.63|1.08|
|||noyouth|-0.19|-0.35|
||Household share of employed|none|0.35|0.9|
|||less than 1/3|-0.01|0.66|
|||between 1/3and 2/3|-0.02|-0.15|
|||more than 2/3|-0.53|-0.59|
||Household share of unemployed|none|-0.12|-0.16|
|||less than half|0.41|1.54|
|||more than half|0.19|1.73|
|Education|Head of household alphabetized|yes|0.79|1.25|
|||no|-1.12|-0.62|
||Household sharewith no education|less than 1/3|0.97|2.18|
|||between 1/3 and 1/2|-0.53|0.71|
|||between 1/2 and 3/4|-1.67|-0.54|
|||more than 3/4|-2.22|-1.44|
||Household share of alphabetized|less than 1/3|-2.06|-1.11|
|||between 1/3and 1/2|-0.47|0.51|
|||between 1/2 and 2/3|0.27|1.67|
|||more than 2/3|1.32|2.89|
|Health|Household access to health|no healthproblems|-0.08|-0.28|
|||total access|0.16|0.5|
|||partial access|-0.17|0.31|
|||no access|-0.34|-0.29|
||Household medical coverage|yes|2|4.31|
|||no|-0.97|-0.32|
||Household share of medical coverage|total coverage|2.1|4.48|
|||at least half household|2.06|4.88|
|||less than half household|1.65|3.54|
|||no coverage|-0.97|-0.32|
|Housing|Type of accommodation|villa|2.73|4.59|
|||apartment|1.82|-0.97|
|||moroccan house|-0.12|2.77|

25
**Table 3: Philippines’ Export, Import and Intensive Margin growth rates (February 2019-December 2020)**

||**E**|**xports (**|**%)**||**I**|**mports (**|**%)**||
|---|---|---|---|---|---|---|---|---|
|**Month**|**Val**|**#Part**|**#Prod**|**IM**|**Val**|**#Part**|**#Prod**|**IM**|
||||**Pre**|**-lockdo**|**wn**||||
|2019m2|-1.14|7.14|1.01|-|-16.61|-4.26|-5.35|-|
|2019m3|15.30|0.00|4.86|14.66|17.42|5.93|4.68|22.78|
|2019m4|-6.26|-5.00|-9.18|-3.55|0.85|-4.90|-1.24|0.58|
|2019m5|9.73|0.58|8.09|7.00|4.23|6.62|0.40|4.18|
|2019m6|-0.82|1.74|-3.16|0.17|-10.76|-7.59|-1.64|-10.36|
|2019m7|1.54|2.86|5.80|2.06|12.51|3.73|1.93|12.80|
|2019m8|0.93|-2.78|-7.12|2.02|-5.86|-4.32|1.31|-7.49|
|2019m9|-3.54|2.29|7.21|-3.80|1.93|2.26|-2.42|2.02|
|2019m10|4.36|0.56|1.83|2.40|4.43|-3.68|1.71|2.68|
|2019m11|-11.45|-0.56|-4.77|-9.88|-6.36|9.16|-2.37|-3.41|
|2019m12|2.28|-1.68|-7.84|0.93|-6.14|-9.79|-2.42|-8.35|
||||**Lo**|**ckdown**|||||
|2020m1|0.67|-4.55|5.13|-0.18|6.66|3.10|2.99|6.03|
|2020m2|-6.72|-5.36|-2.97|-6.46|-24.00|0.75|-13.01|-23.97|
|2020m3|-15.84|0.00|-8.63|-17.36|-2.12|-4.48|1.18|-0.32|
|2020m4|-37.59|-22.01|-31.83|-37.47|-52.53|-12.50|-18.63|-52.72|
|2020m5|60.10|18.55|38.16|59.54|78.35|17.86|19.42|56.76|
|2020m6|18.65|3.40|11.93|22.25|15.56|0.76|7.80|26.72|
|2020m7|5.43|5.92|3.31|7.24|11.54|4.51|2.51|14.63|
|2020m8|-3.18|1.86|-1.77|-2.92|-2.79|0.72|-2.25|-1.19|
|2020m9|13.72|7.93|6.52|13.16|9.69|2.86|2.43|9.91|
|2020m10|0.34|-1.13|3.82|-1.16|0.12|-4.17|0.46|0.82|
|2020m11|-6.68|-4.57|-3.84|-4.87|-5.84|-1.45|-1.03|-5.28|
|2020m12|0.01|-1.80|2.14|0.18|5.54|-1.47|1.12|2.00|

Note: Val=Value, #Part=Number of partners, #Prod=Number of products and IM=Intensive Margin Source: Authors’ compilation using PSA data lockdown period (after the twelfth month). The survival rate after the first month of trading was 47% when product-month spells are considered. A third of these relationships were active after the second month and less than 13% of them were active during the lockdown period.

Next, we explore the effect of the lockdown on export survival at each level. As defined earlier, the lockdown corresponds to the period when the lockdown was active in the Philippines (January-December 2020) and the months before the lockdown (JanuaryDecember 2019). The graph on the top-right quadrant shows that overall export survival 15
# **Table 3: ELA Participation**

## **Means, standard deviations in brackets**

## **P-value of t-test of equality of means with control group in braces**

||**Control**<br>|**Treatment**<br><br>**All**|**Difference**<br>|**Control**<br><br>**Age**|**Treatment**<br><br>**12-17 at B**|**Difference**<br><br>**aseline**|**Control**<br><br>**Age**|**Treatment**<br><br>**18-25 at B**|**Difference**<br><br>**aseline**|
|---|---|---|---|---|---|---|---|---|---|
||**(1)**|**(2)**|**(3)**|**(4)**|**(5)**|**(6)**|**(7)**|**(8)**|**(9)**|
|**_A. Membership_**||||||||||
|**Number of potential ELA members per village**|136.6<br>[132.1]|129.9<br>[9.5]|{.738}|||||||
|**(Club Members) / (pop aged 12-25)**||.307<br>[.175]||||||||
|**_B. Participation_**||||||||||
|**Have you ever heard about ELA clubs?**|.272|.890|{.000}|.241|0.883|{.000}|.301|.897|{.000}|
|**Have you ever participated in any ELA club activities?**|.041|.708|{.000}|.041|.761|{.000}|.041|.657|{.000}|
|**Have you ever participated in Life Skills training**<br>**organized through the club?**||.824|||.832|||.815||
|**Participated in Life Skills training at least weekly**||.772|||.804|||.734||
|**Could recount at least 4 major topics (out of 8) that were**<br>**covered in the Life Skills curriculum**||.512|||.488|||.540||
|**Did you ever received training in financial literacy?**||.247|||.217|||.281||
|**Have you ever participated in Vocational Training**<br>**organized through the club?**||.337|||.319|||.358||
|**Did you take a microfinance loan from BRAC?**||.127|||.088|||.169||
|**Observations**|1,197|3,592||590|1,796||608|1,796||

**Notes:** Data on potential ELA club members in each village is obtained from the village census administered prior to the intervention, while data on club membership was collected during the ELA Monitoring Survey in 2015. Equality of means is tested by OLS regression of the variable of interest on treatment assignment and district fixed effects, with standard errors clustered at the village level. Data on participation in financial literacy training, livelihood skills training and microfinance is conditional on being assigned to the treatment arm that offered those specific programs.
<!-- Start of picture text --> leé Country les Coastal<br>1.0 Historical<br>_oO8 _ 3 Catalogue<br>E ie led f le3<br>= 0.6 yl +] Sad iu | 4<br>UW 2 u is al \<br>fra 1 _ |e . -- | I<br>2 od . w= a - =. z<br>"02i] | H !o} as I.il fe ©o i || Fl }o) as 7i<br>0.0 LT re |’ iu 0 i<br>J FMAM J | & 5 OND ) FMAM |] J AS ONO<br>Month Manth<br>Le5 Northern 1e5 Southern<br>2,0 =<br>— | 3 E Le<br>—e? i 778 t e* al ed<br>Yiode .. 2 - | 27) me el<br>a a l oa “. 1 =.<br>P05 a | cy Me a |<br>r J oJ AS i ry i J J A Ss il<br>0,0 ce ee ee 0 | ee) ere |<br>| FMAM J] | &A §S ON OD ) FMAM | J AS ONO<br>Month Month<br><!-- End of picture text -->
|Latin America and the Cari|bbean (30)||
|---|---|---|
|Antigua and Barbuda*|Dominican Republic|Panama|
|Argentina<br>|Ecuador|Paraguay|
|Bahamas, The*|El Salvador|Peru|
|Barbados*|Grenada*|St. Kitts and Nevis*<br>|
|Belize|Guatemala<br>|St. Lucia*<br>|
|Bolivia|Guyana*|St. Vincent and the Grenadines*|
|Brazil|Haiti*|Suriname*|
|Chile|Honduras<br>|Uruguay|
|Colombia|Jamaica*||
|Costa Rica<br>|Mexico||
|Dominica*|Nicaragua||
|Middle East and North Afri<br>|ca (16)<br>||
|Algeria*|Jordan|Saudi Arabia|
|Bahrain|Kuwait|Tunisia|
|Djibouti*|Lebanon*|United Arab Emirates|
|Egypt, Arab Rep.|Morocco|West Bank and Gaza|
|Iran, Islamic Rep.<br>|Oman*||
|Iraq*|Qatar||
|South Asia (8)<br>|||
|Afghanistan*<br>|India<br>|Pakistan*<br>|
|Bangladesh*|Maldives*|Sri Lanka|
|Bhutan*|Nepal*||
|Sub-Saharan Africa (44)<br>|||
|Angola*<br>|Ethiopia*<br>|Namibia<br>|
|Benin*|Gabon*|Niger*|
|Botswana|Gambia, The*|Nigeria|
|Burkina Faso*<br>|Ghana<br>|Rwanda*<br>|
|Burundi*|Guinea*|Senegal|
|Cabo Verde*|Guinea-Bissau*|Seychelles*|
|Cameroon<br>|Kenya|Sierra Leone*|
|Chad*|Lesotho|South Africa|
|Comoros*|Liberia*|Sudan*|
|Congo, Dem. Rep.*|Madagascar*|Tanzania|
|Congo, Rep.*|Malawi*|Togo*|
|Côte d’Ivoire|Mali*|Uganda|
|Equatorial Guinea*|Mauritania*|Zambia|
|Eritrea*|Mauritius*|Zimbabwe*|
|Eswatini*|Mozambique||

Note: The number of countries is in parentheses next to the country group name. Those in italics are low-income countries (based on the World Bank classification for FY2020). Those with * are not included in the quarterly series. The quarterly data are available in 106 countries.

61
11 effective storage level of the tank (Pavatkulam) and the damage ratio for paddy during those periods. This implies that when the effective storage level of the tank reduces, it is very likely to reduce crop yield and increase the damage incurred in that season. In the 2012 and 2014 Yala seasons, for example, the average effective storage level (from June 1 to September 30) was very low at 23 percent and 54 percent respectively of the average normal level for the same time period (which is 9.54 MCM, based on data over 2012 to 2017). The damage ratios for these seasons were at 36 percent in 2012 and 18 percent in 2014, indicating a strong negative correlation. Moreover, in the 2015 Yala season, the effective storage in Pavatkulam tank was almost double (188 percent) the normal storage level for JuneSeptember and again the damage ratio was strongly negatively correlated, being only 1 percent, which implies a very good harvest. Hence, storage levels can potentially be used as a good proxy for crop losses due to drought in the Yala season for farmers who have access to water from this tank.<sup>23</sup>

**Figure 2. Effective Storage as a % of Gross Storage (MCM) for Pavatkulam Tank in Vavuniya**

<!-- Start of picture text --> 120%<br>100%<br>80%<br>60%<br>40%<br>20%<br>0%<br>10/1/2011 10/1/2012 10/1/2013 10/1/2014 10/1/2015 10/1/2016 10/1/2017<br><!-- End of picture text --> Source: Author constructed based on MCM data from Irrigation Department of Sri Lanka.

Figure 3 shows the strong negative correlation between the average effective storage level (during the Yala season) and the damage ratio for the Yala season in Vavuniya over three seasons for which comparable data are available.

23 Irrigation Department, Ministry of Irrigation and Water Resources Management, Sri Lanka.
## Figure 10

# - **THE TRANSGAS GASANDES OUTCOME**

<!-- Start of picture text --> INVESTMENT ELECTRICllY PRICES<br>YEAR PROYECT INVESTMENT<br>Pipelines:<br>1D95979 Electrogas _ 23<br>Distribua Pon gnrids:2<br>1969 Metrogas<br>1997-98 Energas 44<br>- as a Po 42<br>Cobndcycie plants<br>Z&a-n Is-Ur-o Ti 17<br>1993 1994 19995 1999 1997 1998<br><!-- End of picture text --> SOURCE: NATIONAL ENERGY COMMISION 23
# **Table 22: Cross-country covariates associated with the demographic penetration of branches**

This table summarizes the results of OLS regressions of the (logarithm of) number of branches per thousand adults in commercial banks on different country characteristics. Each column in each panel represents the result of one regression. The definition of each variable is shown in Appendix 1. Robust standard errors are in parentheses. ***= significant at the 1% level; ** = significant at the 5% level; * = significant at the 10% level.

||(1)|(2)|(3)|(4)|(5)|(6)|(7)|(8)|(9)|(10)|
|---|---|---|---|---|---|---|---|---|---|---|
|GDP per capita (log)|0.51***|0.58***|0.50***|0.30***|0.22***|0.48***|0.48***|0.49***|0.49***|0.49***|
||(0.03)|(0.07)|(0.04)|(0.08)|(0.07)|(0.06)|(0.03)|(0.05)|(0.03)|(0.04)|
|Population Density (log)|0.02|0.06|0.01|0.02|-0.00|-0.04|0.02|0.02|0.01|0.02|
||(0.05)|(0.07)|(0.05)|(0.05)|(0.05)|(0.08)|(0.05)|(0.05)|(0.05)|(0.05)|
|Gini||-0.02*<br>(0.01)|||||||||
|Inflation|||-0.01<br>(0.01)||||||||
|Electricity consumption||||0.22**<br>(0.09)|||||||
|Phone lines|||||0.34***<br>(0.07)||||||
|Road density||||||0.13<br>(0.09)|||||
|Contract enforcement|||||||-0.00<br>(0.00)||||
|Absence of violence||||||||0.04<br>(0.09)|||
|Concentration|||||||||-0.16<br>(0.40)||
|Branch approval||||||||||-0.02|
|||||||||||(0.16)|
|Constant|-2.0***|-1.6|-1.8***|-1.8***|-0.3|-2.0***|-1.7***|-1.9***|-1.7***|-1.8***|
||(0.41)|(1.01)|(0.44)|(0.50)|(0.53)|(0.56)|(0.47)|(0.49)|(0.57)|(0.46)|
|N<br>|119|49|115|102|118|84|119|118|115|116|
|R<sup>2</sup>|0.59|0.56|0.60|0.54|0.66|0.68|0.60|0.60|0.58|0.57|

58
l.67

-

-

> Figure 1.C.20 LABOR USES UNDER POPULATION. INCOME AND WAGE GROWTH, 2015-19

<!-- Start of picture text --> (Ultlm of mm-oe")<br>1.4<br>1.32<br>11 1.<br>0.9<br>0.8<br>0.7<br>0.6<br>0.5<br>0.4<br>0.2<br>120 140 160 IS0 200<br>Cocoa Pice (1967 C.4/KSI<br>a PERENAL + AN JAL r A.? LuO<br><!-- End of picture text -->
Household weights are subsequently calibrated using census data from the 2017 census. Let h represent these model-adjusted weights for households. Design-based weights for children are calculated as jj ww (FF) 1 1 = ∗ h e e e e , 3� cc jj ww jj Where e e e e is the number of eligible children in the household. Weights were adjusted via ww (FF) min�nn iterative proportional fitting to reflect population-level margins for Khyber Pakhtunkhwa on the categorical variables taken from the 2017 census described above (e.g., area of residence, roof nn material, etc.). Survey weights were applied to all analyses, with household weights for the descriptives and child weights for the regressions.

# <mark>Key Variables and Tools</mark>

Basic demographic data were collected from each respondent for child gender, age, maternal educational attainment, birth registration (respondents confirming the child was registered were asked to provide proof of the document), and area of residence (i.e., urban, rural, semi-urban, inner-city). All instruments were administered in Urdu. In circumstances where children were unable to speak Urdu, enumerators effectively engaged children using various methods, such as teaching them Qaida in Madrasa.

## <mark>Outcome Variables</mark>

### _<mark>Early Childhood Development Measures</mark>_

_Children 0-35 months._ The Caregiver-Reported Early Development Instrument (CREDI) longform was used for children under 36 months of age. The CREDI is a globally-validated developmental assessment tool (Waldman et al., 2021), and the short-form version of the CREDI has previously been validated and used in Pakistan (Hentschel et al., 2024; McCoy et al., 2018). The tool consists of up to 100 caregiver-reported items developed according to typical abilities by age within the birth to age 3 range in 6-month increments (e.g., for children 0-6 months, 6-12 months, and so forth) and administration ends based on 5 consecutive incorrect answers so as not to distress the child. Items measured children’s developmental status across four primary domains: motor, language, cognition and social-emotional development. An age-standardized Z-score, created by uploading deidentified data to the CREDI application, allows comparison of a given sample to a global, advantaged, sample based on children from 15 low- and middle-income countries.<sup>5</sup> A child scoring more than 2 standard deviations below the mean is typically considered to be developmentally “off-track."

> 5 <mark>The reference sample contains 19,165 children who all have a mother who completed secondary school or higher education and live in a household where least one adult had engaged in 4 or more of the 6 “Play activities” from the Family Care Indicators with the child.</mark>

6
# **1. Introduction**

Policy-makers around the world see infrastructure investment as an essential determinant of growth.<sup>1</sup> However, as Table 1 shows, different regions display different behavior in terms of infrastructure investment and economic performance. For example, Straub et al. (2008) show that between 1975 and 1995 East Asia’s economic growth outpaced the growth of other world regions and also displayed larger rates of infrastructure stocks increase, while the Middle East and North Africa group of countries’ GDP grew only by a factor of 1.8, despite having one of the highest growth rate in terms of both electricity generating capacity and telephone coverage.

**Table 1: Growth of GDP and Infrastructure Stocks**

|**199**|**5 levels as mul**|**tiples of 1975 leve**|**ls**||
|---|---|---|---|---|
||GDP|Electricity|Roads|Telecoms|
|East Asia|4.8|5.9|2.9|15.5|
|South Asia|2.6|4.4|2.5|8.2|
|Middle East & North Africa|1.8|6.1|2.1|7.2|
|Latin America & Caribbean|1.8|3.0|1.9|5.1|
|OECD|1.8|1.6|1.4|2.2|
|Pacific|1.7|2.0||4.3|
|Sub-Saharan Africa|1.4|2.6|1.7|3.9|
|Eastern Europe|1.0|1.6|1.2|6.9|

GDP – PPP constant 2000 international $; Electricity - MW of generating capacity; Roads – km of paved road; Telecoms – number of main lines. See Straub et al. (2008) for construction. Sources: World Development Indicators and Canning (1998).

One possibility is that the growth impact of infrastructure policies might depend on the correct placement of specific investment projects, the composition of infrastructure investments and on their efficiency in relieving infrastructure constraints of the economy as they emerge. As a matter of fact, Table 2 shows that Middle Eastern and North African countries fare badly in terms of a number of infrastructure constraints such as connection delays or electricity outages. It is likely that, in contrast to other regional blocks, the Middle East and North Africa region does not face an infrastructure access gap, but faces an infrastructure quality gap. Indeed, with the exception of Yemen and Djibouti or Iraq, MENA countries have attained universal access in most basic

> 1 See for example JBIC et al. (2005)

2
|**Table 6: Related inference tests**<br>|||
|---|---|---|
|Dependent variable|CERs to 2012|CERs to 2020|
|Model|Steady‐state fl|ows (mtCO2e)|
|Symmetric|575|799|
|Asymmetric|694|1,078|
|Tests of symmetric and asymmetric models|||
|Upper bounds are equivalent|F(1,23)=31.29<sup>a</sup>|F(1,23)=33.00<sup>a</sup>|
|Models are equivalent|χ<sup>2</sup>(1)=1,296<sup>a</sup>|χ<sup>2</sup>(1)=1,323<sup>a</sup>|
|Dependent variable|CERs t|o 2012|
|Model|Symmetric|Asymmetric|
|Probability that cumulative CERs in August 2009<br>exceed 520 mtCO2e|>90%|>99%|
|Upper bound = 520 mtCO2e|F(1,23)=62.64<sup>a</sup>|F(1,23)=67.09<sup>a</sup>|

a Indicates statistical significance at the 1 percent level.

19