rafmacalaba/datause-encoder-data huggingface dataset:
||Panel C:An<br>|nual income in ca<br>|sh (log)<br>|
|---|---|---|---|
|Log of refugees (50km)|-0.0506<br>(0.0556)|-0.0514<br>(0.0559)|-0.0578<br>(0.0537)|
|Observations|16,016|16,016|9,873|
|R-squared|0.1395|0.1403|0.1651|
||Panel D:An<br>|nual income in ki<br>|nd (log)<br>|
|Log of refugees (50km)|0.1382|0.1377|0.1994|
||(0.1866)|(0.1863)|(0.1582)|
|Observations|15,976|15,976|9,861|
|R-squared|0.2589|0.2592|0.2976|
||Panel E:Average<br>|monthly revenue<br>activities (log)|from non-ag.|
|Log of refugees (50km)|-0.0093|-0.0086|-0.0382|
||(0.0578)|(0.0576)|(0.0486)|
|Observations|7,581|7,581|4,676|
|R-squared|0.3054|0.3073|0.3438|
|**_Included controls_**||||
|Exogenous controls (age, sex)|_No_|_Yes_|_Yes_|
|Other controls (HHsize, marital status, education,<br>occupation, income source)|_No_|_No_|_Yes_|
|Year fixed effects|_Yes_|_Yes_|_Yes_|
|Cluster fixed effects|_Yes_|_Yes_|_Yes_|
|Household fixed effects|_No_|_No_|_No_|

To do so, we use the variables capturing whether an individual is a wage worker, self-employed, an unpaid or a family worker.<sup>27</sup> We decompose the previous results from Table 11 into these employment categories by interacting each of the variables with the refugee index.

Table 12: Decomposition of the OLS effects of refugee presence on labor market outcomes, with analysis at cluster level and the distance weighted refugee index at 50km from the clusters (Panel 2009-2012)

||(1)|(2)|(3)|
|---|---|---|---|
||<br>Labor force parti<br>|<br>cipation (12 mo<br>|<br>nths)<br>|
|Log of refugees (50km)|-0.00738|-0.00802|-0.01587**|
||(0.00681)|(0.00669)|(0.00781)|
|Log of refugees(50km)#Wage workers|0.01975***|0.01959***|0.01368***|
||(0.00610)|(0.00607)|(0.00444)|
|Log of refugees(50km)#Self-employed|0.01836***|0.01794***|0.01296***|
||(0.00584)|(0.00568)|(0.00447)|
|Log of refugees(50km)#Unpaid workers|0.00980*|0.00996*|0.00630|
||(0.00587)|(0.00578)|(0.00434)|
|Log of refugees(50km)#Family workers|-0.00049|0.00028|0.00642|
||(0.00451)|(0.00444)|(0.00619)|
|Observations|14,801|14,801|10,146|
|Root MSE|0.208|0.206|0.171|
|**_Included controls_**||||
|Exogenous controls (age, agesq, sex “male==1”)|_No_|_Yes_|_Yes_|
|Other controls (HH size, marital status, education,<br>occupation, other income sources)|_No_|_No_|_Yes_|

27 We ignored the apprenticeship category which has a very low number of observations.

33
|2014|Boyd<br>and<br>Curtis|United States|2006 to 2007|energy<br>expenditure<br>over gross output in<br>percentage|merging survey of frm<br>management<br>practices<br>with US Census data<br>on manufacturing|Better managed frms pro-<br>duce more output and are<br>larger.<br>At best, fndings<br>show a modest relation-<br>ship between management<br>and energy efciency, but<br>standard errors are large.|
|---|---|---|---|---|---|---|
|2019|Grover,<br>Ia-<br>covone,<br>and<br>Chakraborty|Croatia|2017|sales<br>per<br>employee,<br>proft|survey on frm capabil-<br>ities in Croatia, includ-<br>ing a module for cap-<br>turing management ca-<br>pabilities|Manufacturing frms are<br>better managed than ser-<br>vice frms. Better manage-<br>ment is consistently and<br>strongly associated with<br>better performance.|
|2019|Bloom, Bryn-<br>jolfsson, Fos-<br>ter,<br>Jarmin,<br>Patnaik,<br>Saporta-<br>Eksten,<br>Van<br>Reenen|United States|2010 to 2015|total factor productiv-<br>ity|survey<br>of<br>structured<br>management<br>practices<br>in 35,000 manufactur-<br>ing plants|The study fnds large dif-<br>ferences<br>in<br>productivity<br>associated with variations<br>in management practices.|
|2019|Bloom,<br>Iacovone,<br>Pereira-<br>Lopez,<br>Van<br>Reenen|Mexico, United States|2014 to 2015|total factor productiv-<br>ity, proft|survey of management<br>practices,<br>combined<br>with data from Mex-<br>ico’s National Survey<br>on<br>Productivity<br>and<br>Competitiveness<br>in<br>2015|Management<br>score<br>is<br>strongly<br>and<br>positively<br>correlated<br>with<br>frm<br>productivity.|
|2020|Karplus<br>and<br>Zhang|China|March<br>and<br>April 2016|energy efciency, total<br>energy use|two-part<br>survey<br>on<br>management<br>and<br>en-<br>ergy<br>management;<br>electricity<br>use<br>and<br>production information<br>from SECA survey|Moving<br>from<br>the<br>25th<br>to<br>75th<br>percentile<br>on<br>management score reduces<br>electricity<br>intensity<br>by<br>40% in value terms and<br>by 33% in physical terms.|

**47**
# **<u>Table 1. Variable Description</u>**

## **Firm-level variables**

Decentralized Dummy variable equals to one if a firm’s affiliation-level is changed from a higher level government to a lower level government. Source: Annual Survey of Industrial Firms (ASIF). The default source for all variables is ASIF.

- Different city Dummy variable equals to one if located in a different city with the seat of the government with which it is affiliated, and zero otherwise.

- Distance The physical distance (log kilometers) between the firm and the seat of the government with which it is affiliated. Source: ASIF, and Geographic Information System.

- Placebo distance Distance from Shanghai for central SOEs, distance from the largest city within the province (except for the provincial capital) for province SOEs, distance from the largest county-level city within the same prefecture (except for the prefecture seat) for municipal SOEs. Source: ASIF, and Geographic Information System.

Privatized Dummy variable equals to one if state share falls below 50% or exit from the database

- Firm asset Log of firm asset

- ROS Ratio of before-tax profit to sales

- _TFP_ Total factor productivity, calculated as the residual from the regression of log value added on log fixed capital and log employment. Three methods are used, including OLS, Olley-Pakes and index number. Please see the Appendix E.

- Firm importance Ratio of the firm’s valued added to the total value added of firms in the same 2-digit industry and affiliated with the same government

- Fully stateDummy variable equals to one if state share in firm’s equity equals 100% owned

Third front firm Dummy variable equals to one if firm was established during 1964-1966 or 1969-1971 in the _Third Frond Construction Area_ .

31
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22
**Figure 1: Compulsory Instructional Time and Reading Scores (PISA 2012)**

<!-- Start of picture text --> 1,200 600<br>1,000 500<br>800 400<br>600 300<br>400 200<br>200 100<br>0 0<br>Compulsory Instructional Time PISA 2012 Reading Score<br>Hours per year PISA Score<br><!-- End of picture text --> Source: Authors’ construction, using countries with OECD indicators for 2014 and PISA 2012 data, with compulsory instructional time per year calculated as total compulsory instruction time divided by duration of schooling (primary and lower secondary).

Similarly, when looking specifically at Latin America, the same story emerges. Whether using data from countries participating in the Second Regional Comparative and Exploratory Study (SERCE) or in the subsequent evaluation (TERCE), there is no clear relationship between reading scores and instructional time (Figures 2a and 2b). (The SERCE sample is older but has data available for more countries on instruction time.) For example, a decade ago Uruguay and Nuevo León (a state of Mexico) had considerably higher reading scores than Ecuador and Panama, although the latter offer more instructional time (Figure 2a). Today, Mexico and Costa Rica have higher scores but lower instructional time than Peru or Nicaragua (Figure 2b). In short, there is no simple relationship between instructional time and student learning. This lack of a correlation does not rule out a causal impact: Countries may select high hours to compensate for other poor characteristics of their education systems. Or increased hours may only be effective under certain enabling conditions (e.g., school directors and teachers have training to use the time effectively). The design and implementation of extended school day programs is heterogeneous, which may mask the impact of some programs.

5
Parents were informed about the transfers by the staff of the Nahouri Cash Transfer Pilot Project and a project manager who was hired by the research team. This researcher manager participated in all activities related to the project’s logistical organization, and she reported directly to the research team. The transfers were paid in each village at the place where each village usually meets for communal meetings, such as the market square or the village chief’s residence. We avoided locations associated with specific public services such as schools or health centers because we did not want to send the false signal that unconditional transfers might have been tied to these services.

For households randomly assigned to a CCT scheme, for their children under age seven, receiving the transfer required quarterly visits to the local health clinic for preventive health care (growth monitoring), while for children age seven to fifteen, receiving the transfer required school enrollment with an attendance rate above 90 percent each quarter.<sup>10</sup> For families randomly assigned to a UCT program, the mother or father received a quarterly stipend for each child, and there were no requirements or conditions linked with receiving the stipend. Each child in the CCT households was given a program booklet in which school attendance or health clinic visits were recorded by the school principal or health clinic staff, respectively. In addition to recording attendance or clinic visits, the administrators also had to sign and stamp the booklet to verify its authenticity. The booklets were used by the project staff in charge of transfer payments, under the supervision of our project manager, to confirm a child’s satisfaction of the conditionality requirements needed to receive CCTs. In addition, and again under the supervision of the project manager, 20 percent of these children were randomly selected and a village committee that had been specifically trained to do audits verified the information in the booklets against health clinic and school administrative registers. Based on the reports from our project manager and our discussions with these committees, it appears that conditionality was enforced. Cash transfer take-up rates (the fraction of eligible households receiving transfers for at least some children) in the CCT villages declined as the school year progressed, which is also consistent with conditionality being enforced.<sup>11</sup>

> 10 In the CCT villages, the first payment of the school year is conditional on school enrollment and not attendance, since attendance cannot be measured in the holiday period preceding the start of the school year.

> 11 The CCT take-up rates by quarter for school year 2008-2009 are 99.0, 91.0, 90.7, and 85.3 percent, respectively. In school year 2009-2010, the rates are 94.7, 91.6, 89.9, and 89.7 for each quarter, respectively. The take-up rates in the UCT villages are considerably higher. In school year 2008-2009, they are 99.4, 98.8, 98.6, and 94.5 percent for each quarter, respectively. In 2009-2010, they are 99.1, 98.8, 98.5, and 97.1 percent for each quarter, respectively.

7
<mark>quantities rather than export values. The country sample is reduced to the EU countries for which export quantities (in kilograms) are readily available, i.e. it excludes Japan and the United States. The results are reported in Appendix Table 6 and broadly support our findings except for the role of</mark> an exporter’s reliance on imported inputs in a sector which remains positive but loses statistical significance.

# **4. Robustness tests and extensions**

In this section, we present two robustness tests and two extensions. First, we use an alternative measure of the Covid-19 shocks in the exporter and importer country based on industrial production. Second, we study separately the months February to April 2020 to better reflect the period of the global downturn, and the months April to June 2020 where restrictions in most countries were progressively eased. Finally, in our extensions we include China as an exporter and use mirror trade data to expand the sample to 64 developed and developing countries exporters.

## **_Industrial production as an alternative measure of the demand and supply shocks_**

Results presented in Table 2 could potentially be biased by the presence of measurement error in the work and retail mobility variables, which are used to capture respectively the demand and supply shocks induced by Covid-19. In order to control for this, equation (2) is estimated using growth in industrial production as an alternative proxy for production shocks through the GVC channel in the exporter country and in its trading partners. Interacting GVC-related sector intensities with IPI growth is more closely related to the GVC channel than the broader work and retail mobility measures. The results presented in Table 3 hold, confirming the heterogeneous impact of the demand and supply shocks across sectors.

**Table 3: Supply, demand and third-country shocks and bilateral export growth, the role of sector characteristics, OLS regression results, alternative shocks**

|FRAMEWORK|VARIABLES|(1)<br>**_growth_**_ijkt_|(2)<br>**_growth_**_ijkt_|(3)<br>**_growth_**_ijkt_|(4)<br>**_growth_**_ijkt_|
|---|---|---|---|---|---|
|**Exporter supply shock***|**_ipiit*remoteik_**|**1.003****|||**1.122****|
|**sector characteristic**||(0.498)|||(0.520)|
||**_ipiit*gvcil_**|**2.060*****|||**2.343*****|
|||(0.668)|||(0.677)|
|**Partner demand shock***|**_ipijt*durablek_**||**0.499*****||**0.470****|
|**sector characteristic**|||(0.185)||(0.191)|
||**_ipijt*gvc partnerjl_**||**-0.390*****||**-0.394*****|
||||(0.141)||(0.141)|
|**Third country**|**_competition shockijkt_**|||-0.067|0.187|
|**supply shock**||||(0.175)|(0.182)|
||**_upstream shockilt_**|||**1.828****|**2.207****|

20
**Table 10: Opportunity Share of Inequality in Test and Exam Scores Controlling for the Allocation into Different School Systems**

||TIMSS Math test|Primary exam|Preparatory exam|Secondary exam|
|---|---|---|---|---|
|Base results|0.216|0.244|0.306|0.224|
|School system controls|0.211|0.240|0.303|0.180|
|School fixed effects|0.0718||||

Sample: SYPE 2009, samples of 12-14 y.o. primary, 14-18 y.o. preparatory, and 17-21 y.o. secondary graduates; source: SYPE 2009 survey.

The results suggest that, at the primary and preparatory levels, attendance of different school systems contributes very little to inequities in learning achievements. Taking the baseline uncorrected estimates as reference, and using data either from TIMSS test-scores or from the primary and preparatory completion exams in the SYPE survey, the share of achievements inequalities associated with a child’ circumstances remains virtually the same when controlling for attended school system. This suggests that, had they attended the same school systems, the gaps between pupils from different backgrounds would not have differed.<sup>16</sup> On the other hand, tracking into general and vocational secondary schools at the secondary level does explain a significant share of learning achievements inequities. Although circumstances continue to drive large learning differentials, controlling for school type does reduce significantly their effects (the estimated lower bound decreases from 22 to 18 percent). Hence, while a significant share (25 to 35 percent) of pupils from more privileged background attend private schools, we do not observe that this allocation explains a large share of achievement differentials at the primary and preparatory levels. However the tracking of children from different backgrounds into general and vocational schools does seem to contribute to achievements gaps at the secondary level.

> 16 Introducing schools fixed effects does reduce considerably the estimates of the share of unequal opportunities - to 0.0781 for the TIMSS test-score at completion of preparatory -, suggesting that a large part of those inequities is captured by the attendance of different schools. However, this decrease is difficult to interpret as it also includes the effects of living in different neighborhoods (and more generally geography) and related family characteristics.

32
- 22 -

controlling for previous achievement, socioeconomic background and systematic selection by school type. The effect was larger for the higher status F-type schools. In terms of policy, the important question is why this difference exists. Is it possible to identify the characteristics of the private schools that most contribute the achievement effect? What do administrators and teachers do that is different? What are the peer group effects? These questions are answered in the next two subsections.

# S;hool. Classroom. Teacher and Teaching Practice Variables

Table 2 showed that there were substantial differences in the school, classroom, teacher qualification and practice variables among the various types of schools. These differences were not, however, consistently covmensurate with the status of the school type.

Table 6 shows the results of adding these variables to the achievement equations for the various types of schools. With student background characteristics held constant, few of the school, classroom or teaching practice variables were statistically significant. In the F-type schools, the students of the more educated teachers scored about I point higher than did the students of less well-educated teachers for each additional year of teacher education. Further, the students of teachers who taught elsewhere scored about one-tenth of a point higher for each additional class period than did the students of teachers who taught fewer class periods elsewhere. The remaining teacher and teaching practice variables had no effect on student achievement. In the 0-type schools, no teacher or teaching variable was associdted with the differences in achievement. In the public
Table 4 (Continued)

|**_C. Detailed Decomposition_**|_Endowment Effect_|_Male Structural_<br>_Advantage _|_Female Structural_<br>_Disadvantage _|
|---|---|---|---|
|**_Manager's Agricultural Labor Input Use (for Season)_**||||
|Household Male Labor Use ('000 Hours/HA)|0.00983|-0.00139|0.0273|
||(0.0096)|(0.0117)|(0.0200)|
|Household Female Labor Use ('000 Hours/HA)|-0.0206|-0.00434|-0.0335|
||(0.0274)|(0.0175)|(0.0425)|
|Household Child Labor Use ('000 Hours/HA)|0.00199|-0.00351|-0.0113*|
||(0.0024)|(0.0025)|(0.0067)|
|Total Hired Labor Use ('000 Days/HA)|-0.000129|-0.00130|0.0106|
||(0.0007)|(0.0018)|(0.0126)|
|Total Exchange Labor Use ('000 Days/HA)|-0.00345|0.0172|0.0178|
||(0.0053)|(0.0152)|(0.0158)|
|**_Household Characteristics_**||||
|Log [Consumption (Birr)]|0.0301**|0.0827|0.482|
||(0.0149)|(0.1093)|(0.4510)|
|Distance to Closest Market (KM)|-0.0206|-0.0117|0.0497|
||(0.0155)|(0.0214)|(0.0978)|
|Household Size|0.0547**|0.0396|0.104|
||(0.0264)|(0.0383)|(0.1656)|
|Dependency Ratio|-0.0186*|-0.0238|-0.0435|
||(0.0110)|(0.0175)|(0.0435)|
|More than Half of the HH Production Sold †|0.0111|0.00161|-0.00210|
||(0.0100)|(0.0029)|(0.0169)|
|Non-agricultural Labor Income †|-0.00152|0.00181|0.0138|
||(0.0050)|(0.0060)|(0.0348)|
|**_Household Agro-Ecological Zone Classificatio_**_n_||||
|Tropic-Warm/Semiarid †|0.0105|-0.000214|-0.00754|
||(0.0077)|(0.0019)|(0.0084)|
|Tropic-Cool/Semiarid †|0.0272|-0.0476|-0.507***|
||(0.0521)|(0.0315)|(0.1401)|
|Tropic-Cool/Subhumid †|0.0666|-0.0736|-0.882***|
||(0.0650)|(0.0566)|(0.1904)|
|Tropic-Cool/Humid †|-0.0428|-0.0340|-0.550***|
||(0.0379)|(0.0235)|(0.1494)|
|**_Shocks_**||||
|Crop Damage †|0.000373|-0.0150|0.00499|
||(0.0027)|(0.0094)|(0.0374)|
|**Observations**||1,518||

Note: Clustered Standard Errors at EA (Enumeration Area) Level in Parentheses. *** p<0.01, ** p<0.05, * p<0.1 † Denotes a Dummy Variable 27