rafmacalaba/datause-encoder-data huggingface dataset:
for these four categories, the fraction of children who have completed a standard number of years of schooling by age, and a number of variables which are likely to have an effect on schooling and employment decisions, by survey year. Table la presents results for the "full" urban sample (excluding the urban selva, which was not included in the 1991 survey), and for each strata separately. Table lb breaks down the full urban sample into men and women, and compares the first (poorest) and fifth (richest) income quintiles.

I present results on attendance and employment patterns separately for children aged 6 to 11, roughly corresponding to primary school, and children aged 12 to 17, roughly corresponding to secondary school. Tables I a and I b, and Figure 2 show that attendance levels for school-aged children in urban areas are very high throughout: For children aged 6 to 11, the attendance rate is always 97% or higher, while for children aged 12 to 17 it is always 90% or higher. By and large, children go to school in urban Peru. But the extent to which they combine school with work changes dramatically across years. Figure 3 shows that the fraction of children who are working is lower at every age in 1991 than in 1985/86 or 1997. Differences are particularly large for the older children. In the full urban sample, 31% and 21 % of children aged 12 to 17 are both attending school and employed in 1985/86 and 1997, respectively. In 1991, by contrast, only 12% of the children in this age group combine school and work. This pattem holds consistently across strata, for girls as well as boys, and for children in the richest and poorest income quintiles.

The 1988-92 crisis is too recent to evaluate its effect on the total amount of schooling chosen by children. However, the data can be used to assess the extent to which children of a certain age have completed a given number of years of schooling. I do this in two ways. First, I calculate the fraction of children who have completed an appropriate number of years of schooling for their age, by survey year. Children in Peru are expected to start first grade at age 6 or 7. Therefore, unless they start school late or repeat a grade, they should have completed first grade by age 8, second grade by age 9, and so on. I consider children to be "on-track" if they have completed _at least_ this number of school years for their age. Note that this is a generous definition of on-track, as children who start school at age 6 should have completed first grade by age 7.

The second approach I take pools data from the three surveys, and calculates the mean years of schooling completed by children in different birth cohorts. All school-aged children surveyed in 1985/86 were unexposed to the crisis, while all school-aged children surveyed in 1991 were exposed to the crisis. By contrast, there is a discontinuity in exposure for school-aged children surveyed in 1997: Those aged 11 through 17 were exposed to the crisis during their school-aged years, while those aged 6 through 10 were not.

9
# **Table 7. Parent Matchmaking and Reward for Providing Old Age Support**

||**Reward**<br>|**for providing old ag**<br>|**e support**<br>|
|---|---|---|---|
||**OLS**|**OLS**|**2SLS**|
|rural|0.061|0.072|-0.057|
||(0.089)|(0.085)|(0.140)|
|Parent matched|0.022|0.020|0.848**|
||(0.028)|(0.029)|(0.418)|
|Parent matched * rural|0.037|0.032|-0.028|
||(0.035)|(0.035)|(0.451)|
|Other controls as in column (1) of Table 5|Yes|Yes|Yes|
|Religion and ethnic dummies|No|Yes|Yes|
|Number of observations|6,300|6,300|6,300|
|Adjusted R2|0.020|0.024|-0.456|
|Cragg-Donald Wald F stat|||37.9|
|Kleibergen-PaapWalk rk F stat|||18.2|

Note. *, **, and ***: statistically significant at the 10, 5, and 1 percent level. White-corrected standard errors clustered at the township level in parentheses.

42
# **1. Introduction**

Many developing countries face severe electricity constraints driven by various electricity supply- and demand-side factors that limit their economic growth by restricting the productive capacity of industries and firms while also lowering the labor productivity of households that are unable to meet their basic needs. Among the supply-side constraints are low electrification rates, insufficient power generation, and frequent outages that significantly affect the population’s welfare. According to the World Development Indicators (WDI), less than 50 percent of the population in 2018 had access to electricity in nearly 60 countries, with the lowest access rates registered in the Sub-Saharan Africa region, including Burundi (11 percent), Chad (12 percent), and Burkina Faso (12 percent).<sup>2</sup> At the same time, the demand-side challenges, such as high electricity tariffs, connection costs, and low willingness to pay, generate demand shortages and have become equally or more important than the supply-side constraints. For example, Sievert and Steinbuks (2020) found that even with low tariffs and no connection charges, the uptake will still be low because people cannot afford energy-using appliances and, hence, have a low willingness to pay for electricity access. In fact, the share of households in the Sub-Saharan Africa region that lives near the electric grid but has no connection is high, with a median uptake of 57 percent for 20 countries, according to the World Bank.<sup>3</sup> Electricity challenges impose considerable obstacles for private-sector development in low-income countries, along with other infrastructure constraints. According to the World Bank,<sup>4</sup> raising Africa’s infrastructure to some regional or international benchmarks could increase per capita growth by one or two percentage points. Among infrastructure constraints, firms in low-income countries consider electricity as one of the top obstacles for their expansion. In particular, the World Bank Enterprise Surveys Indicators data show that 70 and 50 percent of firms in low-income and lower-middle-income countries, respectively, report electricity as a major constraint for growth.<sup>5</sup> In low-income countries, electricity constraints can be a combination of supply-side factors, such as low electrification rates and low generation capacity, and demand-side challenges, such as high tariffs and connection costs, as mentioned above. Surprisingly, the share of firms reporting electricity as a major constraint to growth was relatively

> 2 The World Bank Group. World Development Indicators database.

> 3 The World Bank Group. 2019. Electricity Access in Sub-Saharan Africa: Uptake, Reliability, and Complementarity Factors for Economic Impact.

> 4 The World Bank. 2010. Africa’s Infrastructure: A Time for Transformation.

> 5 Authors’ calculations using the World Bank Enterprise Surveys data.

2
# **Trade and Economic Growth: Evidence on the role of complementarities for CAFTA‐DR countries**<sup>*</sup>

## **César Calderón**<sup>a</sup> , **Virginia Poggio**<sup>a</sup>

> a _The World Bank, 1818 H Street NW, Washington DC 20433, USA_

### **JEL Classification:** F36, F41, F43

**Key Words:** Trade openness, complementarities, growth

> *This paper was prepared for the “DR‐CAFTA and the Complementary Agenda” project. We would like to thank Pravin Krishna and Rashmi Shankar for valuable comments. The views expressed here are those of the authors, and they should not be attributed to the World Bank, its Executive Directors, or the countries they represent. The usual disclaimer applies.
# **Table S2: Baseline Characteristics of Children by Age Group**

|||-----|Difference<br><br>-----Age 0-5--|Difference<br><br>--------|||-------|Difference<br><br>---Age 6-11-|Difference<br><br>---------|||-------|Difference<br><br>---Age 12-15|Difference<br><br>----------||
|---|---|---|---|---|---|---|---|---|---|
||Mean|(C-T)|P-Value|Mean|(C-T)|P-Value|Mean|(C-T)|P-Value|
||(1)|(2)|(3)|(4)|(5)|(6)|(7)|(8)|(9)|
|Age|2.81|-0.13|0.18|8.36|-0.06|0.47|13.23|-0.03|0.64|
|Female|0.480|-0.031|0.340|0.473|0.011|0.700|0.504|-0.060|0.099|
|Ever sick|0.859|-0.001|0.951|0.752|0.027|0.304|0.672|0.011|0.855|
|Number of times sick|2.838|0.080|0.757|1.972|-0.100|0.486|1.691|0.069|0.639|
|Forgone treatment|0.197|-0.045|0.140|0.183|-0.029|0.273|0.165|0.010|0.835|
|Ever visit health provider|0.854|-0.010|0.677|0.741|0.028|0.302|0.665|0.014|0.699|
|Total number of visits, all providers|5.157|-0.181|0.689|3.469|-0.148|0.648|2.858|-0.026|0.845|
|Total health expenditures|869.31|204.72|0.33|448.80|75.90|0.17|417.63|27.85|0.85|
|Observations|919|--|--|1253|--|--|824|--|--|

_Notes:_ Above are sample averages of selected variables by age groups of children at baseline. Columns 1,4, and 7 show averages for all children within the age category; Columns 2,5,and 8 show the difference in average characteristics between children in the Control group and children in the Treatment group; Columns 3,6, and 9 show the p-value of the difference, conditional on market and round fixed effects. Health providers consist of EMPs, public clinics, pharmacies, private hospitals, private doctors, public hospitals, and laboratory visits. All health and visit variables are reported to be during the past year. All income and expenditure data are in 2008 Cordobas. Children who were not sick in the past year are included as zeros for number of times sick and all visit/spending variables. Forgone treatment in past year due to lack of money was calculated to be zero for children who were not sick in the past year. P-values are based upon robust standard errors, clustered at the family level.*** p<0.01, ** p<0.05, * p<0.1 _Source:_ Authors’ analysis based on data described in paper.

2
**Table 9. Exhaust Emission Regulations in Selected Countries**

|**Country**|**Vehicle type**|**Fuel**|**Effective date**|**Equivalent**<br>**Emission**<br>**Limits**|
|---|---|---|---|---|
|Bangladesh<br>|Light & Heavy Duty<br>Light & Heavy Duty<br>|Gasoline<br>Diesel|2006<br>2006|Euro II<br>Euro I|
|China<sup>(1)</sup>|Light Duty (<3.5t)<sup>(2)</sup>-<br>National|Gasoline<br>& Diesel|1993|ECE 15.03 with<br>higher limits|
||Passenger Cars & Light<br>Duty (Beijing & Sanghai)<br>||July, 1999|Euro I|
|Hong Kong<br>India|Light Duty<sup>(3)</sup><br>Light Duty- National||01/01/2006<br>2000|Euro IV<br>91/441/EEC<sup>(4)</sup>|
||Light Duty-Delhi region||04/00|Euro II|
|Indonesia|Gasoline engines<br>Diesel engines||2005<br>2005|Euro II<br>Euro II|
|Malaysia|Light Duty|Gasoline<br>Diesel|01/01/00<br>01/01/00|94/12/EEC<br>94/12/EEC|
|Nepal<br>Philippines|Light Duty-Imported<br>Light Duty||01/02<br>01/01/97|Euro I<br>ECE R 15-04<sup>(5)</sup>|
||Medium & heavy duty||01/01/97|ECE R 49-01|
|Singapore|Light Duty|Gasoline<br>Diesel|01/2001<br>10/2006|Euro II<br>Euro IV|
|South Korea|Gasoline<br>Diesel|||US procedures<br>ECE R 49|
|Sri Lanka|Gasoline<br>Diesel<br>||01/01/2003|Euro II|
|Taiwan|Passenger Cars<sup>(6)</sup><br>Light duty<sup>(6)</sup>|Gasoline<br>Diesel<br>|07/90|US 1984 Limits<br>US 1984 LDT|
|Thailand<br>Saudi|Light Duty|All<sup>(7)</sup>|25/08/2001|96/69/EC<br>ECE R 15.03|
|Arabia||||equivalent|

- (1) The Chinese State Environment Protection Agency (SEPA) proposed the adoption of EU Directives 91/441/EEC in 2001.

- (2) A government notice, posted on 27 June 2001, required the immediate cessation of production of carbureted vehicles. Production was halted immediately and sales were banned from 1 September 2001.

- (3) Euro 3 or equivalent standards will apply to certain class of vehicles under3.5 tones on or after 1 January 2002. From 1 January 2006, LD diesel must comply with Califorrnia regulations. Euro 4 introduced from 01/01/2006 for vehicles up to 2.5 tones, extending to 3.5 tones from 01/01/2007.

- (4) Employs a modified Indian Driving cycle similar to the ECE15+EUDC cycle, except that the maximum speed is limited to 90 km/h.

- (5) Evaporative emission for spark ignition engines shall not exceed 2.0 grams per test. Crankcase emissions should be eliminated.

- (6) Evaporative emission for spark ignition engines shall not exceed 2.0 grams per test. (7) Proposed to the National Environment Board  for implementation as follows: RM =<1305 kg from January 2003; RM>1305 Kg from 1 January 2004. Implementation of Row B of  98 /69/EC (Euro 4) is under discussion.

Source: CONCAWE (2006) 27
HD. In addition, we test the impact of lower mining export prices on Liberia’s development up to 2030 and how they may impinge on efforts to increase fiscal space for priority spending.

In this note, Section 2 presents the base scenario, preceded by a brief summary of the model and the database. Section 3 covers the alternative non-base scenarios. Section 4 offers some concluding observations. Three appendices provide more detail on model structure, the database, and simulation results (in a set of tables and figures).

# <u>2. MODEL, DATA AND BASE SCENARIO</u>

# _MODEL AND DATABASE_<sup>4</sup>

MAMS (Maquette for MDG Simulations) is a CGE (Computable General Equilibrium) model designed for country-level analysis of medium- and long-run development policies, including strategies for reducing poverty and achieving the Millennium Development Goals (MDGs). Technically, it is made up of a set of simultaneous equations. The model is economywide, providing a comprehensive and consistent view of the economy, including linkages between production and the income it generates, households, the government (its budget and fiscal policies), and the balance of payments. The different agents (producers, household, government, and the nation in its dealings with the outside world) are subject to budgets and their constraints: in each budget, receipts and spending, the latter including savings and net borrowing, are by definition equal. The decision rules of each agent – for producers and households, the objective is to maximize profits and utility, respectively – ensure that these budgetary constraints are respected: for example, in a stepwise process households set aside parts of their incomes to direct taxes and savings, allocating what is left to consumption. For the nation, adjustments in the real exchange rate (in the dollarized model economy via adjustments in the domestic price level) ensure that its external accounts are in balance. Wages, rents and prices play a crucial role by clearing markets for factors and commodities; for commodities that are traded internationally (exported and imported), domestic prices are influenced by international price developments, typically (as in the case of Liberia) assuming that international markets will demand and supply the exports and imports of the country at given world prices.

Over time, production growth is determined by growth in factor employment and changes in factor productivity; growth in capital stocks is endogenous while exogenous growth is imposed for labor and other factors (for labor determined by growth in the population of labor-force age). TFP growth is made up of two components, one that responds positively to growth in government capital stocks and one that, unless otherwise noted, is exogenous.

> 4 For a more detailed presentation of the structure of MAMS and its database, see Appendices 1 and 2, respectively. For a detailed, general presentation of MAMS, see Lofgren et al. (2013).

3
(1) Approximately 5% (risk model 1: arsenic, salinity water-storage depletion combined) to 24% (risk model 2: arsenic and salinity) land area in Bangladesh is exposed to extremely high to high risks of elevated arsenic, salinity and groundwater depletion hazards.

(2) An estimated 4.5% (risk model 1) to 17% (risk model 2) of total population (144 million) of Bangladesh are exposed to extremely high to high risks of elevated arsenic, salinity and groundwater depletion hazards; 2.2 million to 8.6 million of these vulnerable populations are poor.

(3) Nearly a million (risk model 1) to over 4 million (risk model 2) children, who are exposed to extremely high to high risks of multi-hazard groundwater risks are either underweight or stunted.

(4) An estimated 32 km<sup>3</sup> of groundwater is withdrawn annually in Bangladesh for irrigation (90%), and domestic and industrial (10%) use; this volume is equivalent to ~4% of global groundwater withdrawals. Furthermore, an estimated 2.3 million (risk model 1) to over 5.7 million (risk model 2) people, who depend on groundwater-fed irrigation are exposed to extremely high to high risks of multi-hazard groundwater risks.

Finally, we have updated the existing, rather speculative estimates of population exposure to arsenic or salinity contamination individually. Our new estimation using the geospatial risk mapping suggests that nearly 33 million people are exposed to >50 μg/L (Bangladesh drinkingwater standard) of arsenic concentrations, and nearly 26 million are exposed to considerably high (electrical conductivity value >1,500 μS/cm) salinity in groundwater.

# **Contributors**

MS conceived the idea of groundwater risk mapping at the national scape in Bangladesh. MS also designed overall study and prepared groundwater arsenic, salinity and storage datasets with technical support from AZ. GJ and SSH provided extensive inputs to the preparation of the manuscript and commented on several versions of the text. KMA and MRK have provided arsenic-affected population exposure data. All authors read the manuscript and provided their comments on the manuscript.

13
Clausen, J., Heinesen, E., Hummelgaard, H., Husted, L., & Rosholm, M. (2009). The effect of integration policies on the time until regular employment of newly arrived immigrants: Evidence from Denmark. _Labour Economics_ , _16_ (4), 409-417.

Colucci, E., Minas, H., Szwarc, J., Guerra, C., & Paxton, G. (2015). In or out? Barriers and facilitators to refugee-background young people accessing mental health services. _Transcultural Psychiatry_ , _52_ (6), 766-790.

- Conley, T. G., Hansen, C. B., & Rossi, P. E. (2012). Plausibly exogenous. _Review of Economics and Statistics_ , _94_ (1), 260-272.

Connor, P. (2010). Explaining the refugee gap: Economic outcomes of refugees versus other immigrants. _Journal of Refugee Studies_ , _23_ (3), 377-397.

- Cunha, F., & Heckman, J. (2007). The technology of skill formation. _American Economic Review_ , _97_ (2), 31-47.

- Currie, J., Duque, V., & Garfinkel, I. (2015). The great recession and mothers’ health. _Economic Journal_ , _125_ (588), F311-F346.

- Dagnelie, O., Mayda, A. M., & Maystadt, J. F. (2019). The labor market integration of refugees in the United States: Do entrepreneurs in the network help? _European Economic Review_ , _111_ , 257-272.

- Danzer, A. M., & Danzer, N. (2016). The long-run consequences of Chernobyl: Evidence on subjective well-being, mental health and welfare. _Journal of Public Economics_ , _135_ , 4760.

- Di Maio, M., & Nisticò, R. (2019). The effect of parental job loss on child school dropout: Evidence from the Occupied Palestinian Territories. _Journal of Development Economics_ , _141_ , 102375.

- Dreher, A., Fuchs, A., & Langlotz, S. (2019). The effects of foreign aid on refugee flows. _European Economic Review_ , _112_ , 127-147.

- Dreher, A., Fuchs, A., Parks, B., Strange, A., & Tierney, M. J. (2021). Aid, China, and Growth: Evidence from a New Global Development Finance Dataset. _American Economic Journal: Economic Policy_ , _13_ (2), 135-174.

- Dufour, J. M., & Taamouti, M. (2005). Projection‐based statistical inference in linear structural models with possibly weak instruments. _Econometrica_ , _73_ (4), 1351-1365.

- Dustmann, C., Fasani, F., Frattini, T., Minale, L., & Schönberg, U. (2017). On the economics and politics of refugee migration. _Economic Policy_ , _32_ (91), 497-550.

- Esteban, J., Mayoral, L., & Ray, D. (2012). Ethnicity and conflict: An empirical study. _American Economic Review_ , _102_ (4), 1310-42.

23
**DRAFT**

**THIS VERSION – 12/18/2020 1:06 PM**

# **COVID-19 – BUSINESS PULSE SURVEY (COV-BPS)**

## **The World Bank**

## **2020**

### **Phone interview introduction:**

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### **Online interview invitation email:**

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Figure _4: Quoted Intermediation Spreads (IFS 60P-60L)_ <!-- Start of picture text --> Quoted Intermediation Spread, 1975-97<br><!-- End of picture text --> <!-- Start of picture text --> Percentiles<br>14<br>12<br>10 Ei Median<br>8 + Istquartile<br>3rd quartile<br>6 la 1st decile<br>4 9th decile<br>2<br>7 8 8 9 9<br><!-- End of picture text --> <!-- Start of picture text --> Quoted Intermediation Spread, 1975-97<br>Medians<br><!-- End of picture text --> <!-- Start of picture text --> 8.<br>7<br>6<br>5 6 Industrial<br>+Developing .<br>4<br>3<br>2 F<br>75 80 85 90 95<br><!-- End of picture text --> <!-- Start of picture text --> Quoted Intermediation Spread, 1975-97<br>Median and Dispersion<br>7 12<br>10<br>6<br>8<br>5 6 6 EiMedian<br>4 W rlr _~~~~~~~~~~~~~~~~~~.p F90th-1 Oth % il e<br>4<br>4<br>2<br>3 0<br>75 80 85 90 95<br><!-- End of picture text --> <!-- Start of picture text --> 34<br><!-- End of picture text -->