rafmacalaba/datause-encoder-data huggingface dataset:
**Figure 4.2:** **_LAYS_ using PISA assessment of 15‐year‐olds**

<!-- Start of picture text --> Years of schooling LAYS (learning starts at age 6)<br>LAYS (learning starts at age 3) LAYS (learning starts at age 0)<br>15<br>10<br>Years<br>5<br>0<br>Albania Algeria Argentina Australia Austria Belgium Brazil Bulgaria Canada Chile Colombia Costa Rica Croatia Cyprus Czech Republic Denmark Dominican Republic Estonia Finland France Germany Greece Hong Kong SAR, China Hungary Iceland Indonesia Ireland Israel Italy Japan Jordan Kazakhstan Korea, Rep. Latvia Lithuania Luxembourg Macao SAR, China Malaysia Malta Mexico Moldova Netherlands New Zealand Norway Peru Poland Portugal Qatar Romania Russian Federation Singapore Slovak Republic Slovenia Spain Sweden Switzerland Thailand Trinidad and Tobago Tunisia Turkey United Arab Emirates United Kingdom United States Uruguay Vietnam<br><!-- End of picture text --> Source: Authors’ analysis of PISA 2015 and Barro‐Lee data.

Notes: Numeraire country is Singapore. Illustration for case where learning starts at Age 6. Correlation coefficients between the three _LAYS_ measures exceed 0.99. Spearman rank correlations between the three LAYS measures exceed 0.99.

There are 26 countries and economies that participated in the 2015 rounds of both TIMSS and PISA. For these countries we can calculate _LAYS_ using both approaches and compare the estimates. We do not expect major differences across the approaches since the scores are similar; the mean TIMSS mathematics score for these 26 countries is 505 (standard deviation 55.7), the mean PISA mathematics score is 477 (standard deviation 45.6), and the correlation between the two is 0.91.<sup>15</sup>

> 15 The fact that the average scores, and their standard deviations, are similar is not surprising.  Both assessments were originally normalized to have mean 500 and standard deviation 100. For TIMSS, normalization was originally done for the (mostly high-income) countries that participated in the 1995 assessment.  For PISA’s math assessment, the normalization was done over the OECD countries that participated in 2003 (for reading, the year used was 2000).

20
fects on agricultural practices. Adding the in-kind grant had important short-term effects with improved practice adoption and productivity but only during the intervention, with adoption returning to pre-intervention levels afterwards.

However, we find that farmers with greater autonomy (defined as the ability to choose how the in-kind grant was spent) showed substantially greater persistence relative to farmers without such autonomy. We provide some evidence that farmers with greater autonomy had greater trust in the project partners, remembered the recommendations better and also exhibited a more positive attitude towards experimentation after the intervention ended. The results suggest an important instrumental role for autonomy in increasing adherence to externally provided recommendations. We consider examining the interaction between the exercise of autonomy and the adherence to expert advice a fruitful avenue for research.

# **References**

- Anderson, J. R. and Feder, G. (2007). Agricultural Extension. _Handbook of Agricultural Economics_ , 3:2343– 2378. [4]

- Ashraf, N., Giné, X., and Karlan, D. (2009). Finding Missing Markets (and a disturbing epilogue): Evidence From an Export Crop Adoption And Marketing Intervention in Kenya. _American Journal of Agricultural Economics_ , 91(4):973–990. [1]

- Baird, S., McIntosh, C., and Özler, B. (2019). When The Money Runs Out: Do Cash Transfers Have Sustained Effects On Human Capital Accumulation? _Journal of Development Economics_ , 140:169 – 185. [6]

- Bardhan, P. (2000). Irrigation and Cooperation: An Empirical Analysis Of 48 Irrigation Communities In South India. _Economic Development and Cultural Change_ , 48(4):847–865. [5]

- Bartling, B., Fehr, E., and Herz, H. (2014). The intrinsic value of decision rights. _Econometrica_ , 82(6):2005– 2039. [2]

- Beaman, L., BenYishay, A., Magruder, J., and Mobarak, A. M. (2018). Can Network Theory-Based Targeting Increase Technology Adoption? Technical report, National Bureau of Economic Research Paper 24912. [4]

- Benhassine, N., Devoto, F., Duflo, E., Dupas, P., and Pouliquen, V. (2015). Turning A Shove Into A Nudge? A “Labeled Cash Transfer” For Education. _American Economic Journal: Economic Policy_ , 7(3):86–125. [6]

- Black, S. E. and Lynch, L. M. (2001). How to Compete: The Impact of Workplace Practices and Information Technology on Productivity. _The Review of Economics and Statistics_ , 83(3):434–445. [5]

- Bloom, N., Eifert, B., Mahajan, A., McKenzie, D., and Roberts, J. (2013). Does management matter? evidence from india. _The Quarterly Journal of Economics_ . [3, 4]

- Bold, T., Kaizzi, K. C., Svensson, J., and Yanagizawa-Drott, D. (2017). Lemon Technologies And Adoption: Measurement, Theory And Evidence From Agricultural Markets In Uganda. _The Quarterly Journal of Economics_ , 132(3):1055–1100. [1]

- Bonin, J. P., Jones, D. C., and Putterman, L. (1993). Theoretical And Empirical Studies Of Producer Cooperatives: Will Ever The Twain Meet? _Journal Of Economic Literature_ , 31(3):1290–1320. [5]

26
Figure 3. Mexico displays high levels of spatial inequality across states _Marginalization Index by state, 2020_ <!-- Start of picture text -->
1.00<br>0.90 0.860.85 0.83<br>0.80<br>0.80 0.73<br>0.69<br>0.70<br>0.60<br>0.60<br>0.50<br>0.490.440.40<br>0.40<br>0.30<br>0.20<br>0.10<br>0.00<br>NL CDMX COA AGC JAL COL BCS SON BC TAM QRO MEX QROO SIN CHH TLA MOR ZAC GTO SLP DGO TAB MICH HGO CAM PUE NAY YUC VER OAX CHS GRO<br><!-- End of picture text -->

Source: Own estimates using CONAPO estimates based on Population and Housing Census 2020. Abbreviation codes in Annex 1

# 3. Literature review

**The literature analyzing migration determinants and their economic effects is quite extensive and can be split into two main research areas: macro and micro approaches.** On the one hand, the migration literature studies how macroeconomic variables determine the intensity and the direction of migration flows, using gravity-type models to estimate the effects of demographic and economic push and pull factors at the aggregated level.<sup>4</sup> On the other hand, the individual migration decision is explored by another research stream that determines the propensity to migrate driven by individual characteristics of the migration unit (household or individual). While most of the literature focuses on international migration,<sup>5</sup> less is known about the factors that determine internal migration behavior and the implications for an individual’s wellbeing.

## **Unlike international migration, other factors may play a more relevant role in determining migration**

**decisions.** For instance, one of the main barriers to international migration for the poorest people is associated with the high costs of migration (Angelucci, 2015). However, the costs of internal migration are lower, and, indeed, the empirical evidence shows that this is a less relevant factor affecting internal migration decisions. Dustman and Okatenko (2014) find diverse implications for emigration probabilities when considering any kind of migration (international or internal) in developing economies. According to the authors, the emigration probabilities decrease with the individual wealth in Latin America. At the same time, they increase in Asia and Sub-Saharan Africa (poorest regions), finding that additional factors such

> 4 Ravenstein (1885) has pioneered analyzing migration flows from this perspective.

> 5 See Hanson (2010) for a review of the evidence of international migration for developing economies, or Hatton and Williamson (2002) for an assessment of the economic and demographic fundamentals that drive international migration to and from developed economies in Europe and the U.S, and within Africa. More recent evidence on the determinants of international migration is explored by Ozden et al (2017), among others.

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Table 12: IV estimates of the effect of ICT use on white/blue-collar workers and wages: Services and Commerce

||Servi|ces|Com|merce|
|---|---|---|---|---|
||(1)|(2)|(3)|(4)|
|**Dependent variable:** ∆**white/blue-colla**|**r workers**||||
|∆Share of labor that uses computers|-0.0000|0.0003|0.0001|0.0001|
||(0.0008)|(0.0013)|(0.0001)|(0.0001)|
|F- frst stage|82.53|21.79|1,067.39|248.45|
|Sargan p-value|0.9323|0.8237|0.6712|0.8552|
|∆Share of labor that uses Internet|0.0002|0.0010|0.0001|0.0001|
||(0.0006)|(0.0016)|(0.0001)|(0.0002)|
|F- frst stage|98.06|9.56|1429.109|301.285|
|Sargan p-value|0.2055|0.1426|0.3203|0.2434|
|∆ln(stock of computer equipment/worker)|0.0032|0.0156|0.0135|-0.0335|
||(0.0178)|(0.0300)|(0.0242)|(0.0396)|
|F- frst stage|88.88|31.72|30.77|5.63|
|Sargan p-value|0.2881|0.2442|0.6094|0.8005|
|**Dependent variable:** ∆**white/blue-colla**|**r wages**||||
|∆Share of labor that uses computers|-0.0227***|-0.0237**|0.0075***|0.0039***|
||(0.0047)|(0.0095)|(0.0007)|(0.0013)|
|F- frst stage|82.53|21.79|1,067.39|248.45|
|Sargan p-value|0.5215|0.8502|0.1527|0.2072|
|∆Share of labor that uses Internet|-0.0201***|-0.0303**|0.0069***|0.0038***|
||(0.0041)|(0.0133)|(0.0007)|(0.0013)|
|F- frst stage|98.06|9.56|1429.109|301.285|
|Sargan p-value|0.3771|0.5095|0.1694|0.2227|
|∆ln(stock of computer equipment/worker)|-0.6464***|-0.3818**|1.3305***|-2.0795***|
||(0.1219)|(0.1604)|(0.2244)|(0.5474)|
|F- frst stage|88.88|31.72|30.77|5.63|
|Sargan p-value|0.3011|0.2424|0.1642|0.3015|
|**Controls**|||||
|∆ln(capital/worker)|No|Yes|No|Yes|
|Age dummies|No|Yes|No|Yes|
|Size dummies|No|Yes|No|Yes|
|Observations|6,241|6,241|8,333|8,333|

Robust standard errors in parentheses _∗ ∗∗ ∗∗∗ p <_ 0 _._ 1, _p <_ 0 _._ 05, _p <_ 0 _._ 01 Source: Authors’ calculations with data from the 2009 and 2014 Economic Censuses, INEGI.

37
**Table 3: Workplace Mobility, COVID Cases, and NPIs, Global Sample**

|<br>|(1)<br>LIC|(2)<br>LIC|(3)<br>LMIC|(4)<br>LMIC|(5)<br>UMIC|(6)<br>UMIC|(7)<br>HIC|(8)<br>HIC|
|---|---|---|---|---|---|---|---|---|
|K-12 closure|3.13|0.04|1.24|0.64|-6.62|-10.60**|-10.20***|-13.32***|
||(4.83)|(3.03)|(4.61)|(5.11)|(4.80)|(3.90)|(3.16)|(3.85)|
|Close N.E. business|1.00|-0.80|-8.83*|-9.30|-3.96|-8.59**|-4.73|-8.75***|
||(7.40)|(4.45)|(5.01)|(5.61)|(3.37)|(4.09)|(2.84)|(2.90)|
|Cancel public events|-9.77|-6.37|-5.26|-6.66*|-1.49|-5.66|-2.32|-6.35*|
||(5.27)|(4.46)|(3.88)|(3.75)|(5.96)|(4.45)|(3.04)|(3.16)|
|Close public transp.|-16.51*|-16.17*|-2.20|-5.35|-5.37*|-4.93|-5.06|-6.44**|
||(8.37)|(7.18)|(4.93)|(5.02)|(2.86)|(3.64)|(3.03)|(2.71)|
|Public info. camp.|0.77|-0.40|9.90***|10.47***|7.32|8.99**|4.71*|5.59**|
||(3.23)|(3.35)|(2.89)|(2.31)|(4.91)|(4.07)|(2.62)|(2.70)|
|Restr. on internal mov.|-1.21|-1.85|-12.03***|-10.52**|-14.32***|-16.81***|-2.72|-5.53**|
||(3.57)|(3.13)|(2.98)|(3.81)|(3.78)|(4.46)|(2.04)|(2.18)|
|Log cases|-0.03|-2.43*|-4.30***|-5.57***|-1.50|-3.85***|-4.61***|-3.42***|
||(1.89)|(1.17)|(1.13)|(0.56)|(1.63)|(0.80)|(0.97)|(0.75)|
|Constant|3.76|14.08**|-5.82|6.46|-0.50|8.68*|-1.73|10.41***|
||(3.41)|(5.84)|(4.46)|(4.02)|(5.05)|(4.75)|(2.58)|(2.49)|
|Time FE|Yes|No|Yes|No|Yes|No|Yes|No|
|Day of the week FE|No|Yes|No|Yes|No|Yes|No|Yes|
|Country FE|Yes|Yes|Yes|Yes|Yes|Yes|Yes|Yes|
|# of Countries|8|8|24|24|29|29|40|40|
|Obs.|193|193|720|720|945|945|1777|1777|
|R-squared<br>|0.69|0.62|0.77|0.73|0.85|0.80|0.86|0.80|

Notes: Regression of Google measure of work-related mobility on NPIs, the log of national cases, country, and days of the week/time fixed effects. Robust clustered errors are in parenthesis. *** p<0.01, ** p<0.05, *<0.1. LIC, LMIC, UMIC, and HIC stand for Low Income Countries, Lower Middle Income Countries, Upper Middle Income Countries, and High Income Countries, respectively. See Table AIII for income group classification.

# **V. Mapping to Economic Activity**

Do these voluntary declines in Google mobility in fact map to economic activity? Preliminary evidence from the United States and Sweden suggests they do. Figure 4 presents restaurant reservations by state against COVID incidence for the United States. What is immediately clear is that the fall in reservations predated the closing of nonessential businesses. This is confirmed by Table 4 which suggests a combined elasticity of over 10 and virtually no impact of business closing measures. That is, the entire fall can be accounted for by the increase in cases. The results suggest that what slowed economic activity was not the NPIs, but rather voluntary demobilization as evidence of the magnitude of the threat accumulated.

In the same vein, Figure 2b presents preliminary national data from movie theater releases and revenues in Sweden, which imposed a 50-person limit on gatherings and suggested closing of non-essential businesses only on March 30<sup>th</sup> . By March 17<sup>th</sup> , revenues had fallen to almost zero while releases continue unaffected. Since the data are at the national level, we cannot document these trends more formally.

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The rest of the paper is organized as follows. In Section 2, we discuss details behind our focus on just two countries, El Salvador and Georgia, and provide information about government support measures in these countries. Section 3 provides a general overview of the methods that we use for analysis. Section 4 describes data and main variables. Sections 5 and 6 report results from matching and panel estimation, respectively. Section 7 briefly concludes.

# **2. Selection of Countries and Government Support Measures**

In this section, we explain the reasoning behind focusing on El Salvador and Georgia to study the effect of government support during the COVID-19 crisis. We also provide a brief summary of support measures taken by the governments in these two countries.

As of June 2021, the World Bank Enterprise Surveys (WBES) team has made the COVES data available across 44 countries.<sup>3</sup> Several research papers have already analyzed this rich data in a cross-country context to study the effects of the pandemic (e.g., Muzi et al. 2021), including exploration of the effectiveness of government policies (e.g., Cirera et al. 2021). We started with the understanding that details of government policy, which varied tremendously across countries, would make cross-country analysis extremely complicated. This is not least because the types or sizes of government support varied across countries (as some of these variations can be accounted for through country fixed effects). More importantly, the targeting of policies varied drastically across countries and often used the severity of the firms’ experience with the pandemic as part of eligibility criteria. This has direct implications on the identification of the causal link between government policy and firms’ performance. In particular, the specificity of eligibility criteria to time-varying features of firms, coupled with the absence of that information in our database at the point of firms’ application for the policy, makes it nearly impossible to form a proper control group to consistently estimate the effectiveness of government support. Consequently, our first criterion for selecting the country for analysis is to ensure that government support was not directly conditional on firms’ experience of the pandemic and instead was general, perhaps with some targeting across firm sizes or sectors (typically defined at some pre-pandemic point in time as governments tried to avoid fraud). Our second condition is about the number of

> 3 Please see https://www.enterprisesurveys.org/en/covid-19 for more details on coverage and ongoing data collection projects.

6
education service delivery. The committee supports this improvement by: (1) providing _advice_ to assist in determining the school program and policy; (2) providing _support_ such as financial support, ideas, and activities for the implementation of the school program; (3) providing _control_ over school programs for transparency and accountability; and (4) providing _mediation and communication_ between the school and the community.

Compared to international practices particularly in Latin America, the practice of SBM in Indonesian is very much limited in public schools. The majority of the teachers are public civil servants, whom the schools have no power to hire or fire. In theory, schools have only control over non-salary operational expenditures, even though the discretionary resources are commonly used to hire additional contract teachers.

International partners have supported SBM in Indonesia for more than 10 years. The Ministry of National Education (MoNE) has even established a SBM Secretariat to integrate programs. The donor programs that can be broadly categorized as supporting SBM include: Creating Learning Communities for Children (CLCC) with UNICEF and UNESCO; Decentralized Basic Education Project (DBEP) with Asian Development Bank; Regional Education Development and Improvement Program (REDIP) with JICA; Managing Basic Education and Decentralized Basic Education (DBE) with USAID; and Australia Indonesia Basic Education Program (AIBEP) with AusAID.

There is a small but growing research literature devoted to school-based management in Indonesia. The evidence on school committees is based mainly on qualitative case studies across a range of provinces. The SMERU Institute studies from 2005 and 2008 focused on school committee participation in school management, as well as the school budgeting process that accompanies receiving the BOS block grants (RAPBS). They found school committees were often dominated by school principals, with the help of selected teachers. Committees generally concentrated in raising funds for the school to use on facilities, and rarely entered into areas related to teaching and learning. Participation within the committees was limited, and often dominated by the committee leader, who may be a respected figure in the community (such as a former school principal). However, these committees were seen as potentially effective communicators of complaints from parents about actions within the school, including disciplinary episodes.

5
chance (2 out of 15 tests for the joint tests of all coefficients are equal to zero are significant at the 10% level).

Program implementation data were also collected to better understand the quality of services delivered. These included detailed monitoring data from CGA and qualitative surveys with implementing agencies and program participants. 37 semi-structured qualitative interviews were conducted with program participants at different stage of the program, a qualitative surveyor was also regularly sent with the CGA advisors to assess if the study design was respected (29 surveys). In addition, 61 qualitative interviews were conducted with business owners not selected for the program to monitor potential externalities of the program. Finally, focus groups were conducted with the main implementing agencies (CGA, GUFE and both commercial banks).

# **4 Program implementation and take-up**

Data from the implementing partner, in addition to quantitative and qualitative data, suggest that the program was implemented consistently with the study design. Treatment allocation was respected for all businesses, and all components of the program were effectively offered to almost all program beneficiaries. The program was implemented on a rolling basis: CGA advisors started to reach out to informal businesses in April 2014, and completed both visits in February 2015.

Between April 2014 and January 2015, 2,399 “first visits” (100% of total) were attempted by CGA. The take up rate for the first visit was remarkably high and 2,344 visits were completed with success (98% of total). First visits were considered as not completed successfully when CGA advisers were not able to locate the business. Between April 2014 and February 2015, all businesses who received a first visit in treatment groups 2 and 3 were offered a second visit by CGA. Only 932 of these second visits were completed with success (44% of total). According to qualitative surveys and focus groups with the CGA, the main reasons for this relatively low take-up rate were that many businesses were not interested by the second visit, or did not have time to receive it. This finding is consistent with McKenzie and Woodruff (2014) who find an average attendance rate of only 65 percent for business training programs in developing countries. Most of the group training sessions applicable to businesses in treatment groups 2 12
**<u>Table 2.  Poverty Headcount Ratios—with & without current pension transfers</u>**

|**Country**|**All**<br>**HH's**|**All**<br>**HH's, in**<br>**absence**<br>**of**<br>**current**<br>**transfers**|**Elderly**<br>**individuals**|**Elderly**<br>**individuals**<br>**in absence**<br>**of current**<br>**transfers**|
|---|---|---|---|---|
|**Argentina**|0.21|0.33|0.13|0.55|
|**Bolivia**|0.23|0.31|0.29|0.52|
|**Brazil**|0.22|0.33|0.06|0.52|
|**Chile**|0.16|0.23|0.15|0.39|
|**Colombia**|0.24|0.29|0.31|0.47|
|**Costa Rica**|0.18|0.22|0.32|0.48|
|**Domin Rep**|0.18|0.27|0.22|0.41|
|**Ecuador**|0.19|0.25|0.25|0.39|
|**El Salvador**|0.17|0.29|0.17|0.43|
|**Guatemala**|0.22|0.3|0.24|0.35|
|**Honduras**|0.28|0.39|0.31|0.52|
|**Mexico**|0.19|0.24|0.28|0.43|
|**Nicaragua**|0.2|0.25|0.17|0.29|
|**Panama**|0.23|0.34|0.23|0.51|
|**Paraguay**|0.23|0.3|0.23|0.41|
|**Peru**|0.21|0.24|0.23|0.3|
|**Uruguay**|0.17|0.33|0.08|0.51|
|**Venezuela**|0.18|0.24|0.21|0.38|

Source: Authors’ calculations using data from the Socio-Economic Database for Latin America and the Caribbean (CEDLAS and World Bank) OECD adjusted HH sizes are used to calculate the poverty headcount ratios. The poverty line is 1⁄2 of the national median per capita income in the surveys.

_Poverty Rates without Transfers_ Table 2 compares poverty rates (headcount ratios based on a 1⁄2 median income poverty line) in those 18 Latin American countries for the elderly. The first columns show the poverty rates under the actual situation and the second set of columns the poverty rates without transfers. Computations are shown for both the elderly and the whole population.

The incidence of social transfers appears clearly particularly when the post transfer poverty rate is low: Argentina's poverty rate falls from around 55 to 13 percent.

13