rafmacalaba/datause-encoder-data huggingface dataset:
<!-- Start of picture text --> California schools Education - PISA<br>.o3 SS a | s a|<br>8 / \<br>2 a8 \<br>B14 /\ s \<br>\<br>2<br>gs ! % S8 t<br>5 5) | 5<br>a TM~ as \<br>8 a<br>=\<br>82 > SN 8oS yN<br>~~—_ ‘~~<br>o = o te<br>0 5,000 10,000 15,000 20,000 0 5,000 10,000 15,000<br>Inefficiency Inefficiency<br>——— One output —-——-— Multiple outputs ——— One output —-——-— Multiple outputs<br>kernel = epanechnikov, bandwidth = 495.1830 kernel = epanechnikov, bandwidth = 992.3664<br>3 nsOECD healthns systems o ee nnVietnam(hospitals<br>87 ><br>oN | ~ X<br>fom j \<br>s| j/ ~ \<br>So “XN N<br>= 2 \ 8 \N<br>a NX 2z- XY<br>8 2 X<br>\<br>gs . é \<br>8\8<br>\<br>\<br>“\<br> “a —— a<br>oO o tT<br>0 2,000 4,000 6,000 0 200 400 600 800 1,000<br>Inefficiency Inefficiency<br>———_ One output —-—-— Multiple outputs ——— One output ——-— Multiple outputs<br>kernel = epanechnikov, bandwidth = 158.7086 kernel = epanechnikov, bandwidth = 32.1361<br>TheNotes:OECDCaliforniahealthdatasystemreferdatato 313referunifiedto 29 countries,school districts,and coverand thecoverperiod the period1960-2005. 2003/2004The Vietnam- 2008/2009.data Therefer PISAto 795 datadistrictreferhospitals to 49 countries,with betweenand cover50 and some500 orbeds,all theand yearscover 2000, the 2003period and1998-2000. 2006.<br><!-- End of picture text -->
2) and 2019 (regression 3). Relying on the same years in which data is available for cloud and using its same long differences specification, also leads to consistent results (regression 4). In Table A4 we show the effect of the AIA on investment holds irrespective of whether we define the treatment status of the firm using a 2-year average based on the years 2006/07 (regression 1), or a 3-year average based on the years 2005/06/07 (regression 3).

In contrast to its effects on investment, AIA eligibility reduces the propensity to adopt cloud, in this case by 16.5 percentage points. The magnitude of the estimated coefficient is relatively large compared to the mean rate of cloud adoption, of 28% in our sample. These results reinforce the idea that firms view IT capital investment and purchases of cloud IT services as substitutes – a reduction in the relative price of IT capital leads to a substitution away from cloud services and towards tangible IT investment.

**Table 3:** Effects of AIA on total capital investment and cloud adoption, 2007-2013

|Regressions|(1)|(2)|
|---|---|---|
|Dependent variable|Total Investment|Cloud|
|AIA Dummy|0.481***|-0.165***|
||(0.105)|(0.040)|
|Observations|21,757|2,200|

Note: Time period: Total investment regressions use annual data for the years 2007 to 2013. Cloud regressions use data for 2007 and 2013. Total investment is in (log) thousands of pounds, while cloud is a dummy variable. Treated firms are identified taking the average investment values for firms in 2005/06 and calculating if this lagged investment average is above or below the AIA threshold in any given year. Regressions use a binary AIA eligibility indicator. Regression 1 is estimated using the method outlined in Callaway and Sant’Anna (2021) and regression 2 uses OLS. All regressions include year and firm fixed effects, as well as firm controls of lagged employment, a multi-plant dummy, foreign owned dummy and log age. These are not reported for brevity. Robust standard errors in parenthesis are clustered at the firm level. ***, ** and * indicate significance at the 1%, 5% and 10% level, respectively.

To give a sense of the aggregate slowdown in cloud use induced by the AIA policy, we repeat our baseline estimation applying sampling weights.<sup>33</sup> The weighted regressions show

> 33These aggregate estimates are approximate as they do not account for general equilibrium effects, and some sectors and firms with fewer than 10 employees are not surveyed in our data. The results are available

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# **APPENDIX – INFORMATION ON DATA**

# **COUNTRIES INCLUDED**

**Fast growing countries:** Botswana, Indonesia, Korea, Malaysia, Mauritius, Singapore, Thailand.

**Comparison group:** Chile, Costa Rica, Mexico, the Philippines, Turkey, Uruguay and Venezuela

# **YEARS INCLUDED BY COUNTRIES**

The years included are according to the data availability of government budget items.

Botswana: 1970-96 and 2001-2005 Indonesia: 1972-1999 and 2001-2005 Korea: 1970-97 and 2001-2005 Malaysia: 1972-87 and 2001-2005 Mauritius: 1973-2005 Singapore: 1972-2005 Thailand: 1972-2001 Chile: 1972-2001 Costa Rica: 1972-2001 Mexico: 1972-2000 Philippines: 1972-2001 Turkey: 1970-2001 Uruguay: 1972-2001 Venezuela: 1970-2001

# **LIST OF VARIABLES**

# **1. Control Variables**

**Private Investment** : Gross fixed capital formation by the private sector includes land improvements (fences, ditches, drains, and so on); plant, machinery, and equipment purchases; and the construction of roads, railways, and the like, including schools, offices, hospitals, private residential dwellings, and commercial and industrial buildings. Source: WDI and IFC working paper.

**Imports of goods and services (% of GDP)** : Imports of goods and services represent the value of all goods and other market services received from the rest of the world. They include the value of merchandise, freight, insurance, transport, travel, royalties, license fees, and other services, such as communication, construction, financial, information, business, personal, and government services. They exclude labor and property income (formerly called factor services) as well as transfer payments. Source: WDI.

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positive influence on transport costs while others could have an adverse effect. First, as Table 1 shows the United States is a major market for most Caribbean countries and in 1985 absorbed almost 60- per cent of their total exports. There is, however, considerable variation among individual countries as St. Lucia, St. Vincent, Dominica, Guyana and Grenada maintained major trade contacts with the Europear. Community. Second, geographic proximity to the United States should be a positive factor to the extent that distance influences freight costs. 4/ However, potential orfsetting factors are that technical studies have concluded a need exists for regional port improvements, rationalization of cergoes, and adoption of unitized vessel systems on some major Caribbean routes (UNCTAD 1984). Third, there have been charges of "discrimination" in liner conference freight rates facing Caribbean countries and this study will attempt to determine if there is evidence that such charges may be justified. 5/ Fourth, this study will analyze and compare transport costs for shipments made by air as opposed to vessel. The objective

- 4/ Studies by Bryan (1974), Lipsey and Weiss (1974) or Moneta (1959) have employed regression techn;ques to "explain" transport cost differences between countries. These studies show that distance has a strong significant positive effect. Other variables that are also generally significant include: stowage factors for individual products; the size of individual consignments; unit valves of goods being shipped; and dummy variables for competition between liLers and tramps.

- 5/ As a general proposition, discrimination in ocean frieght rates or services may be held to exist when the liner charge for a particular product contributes more (or less) to the costs and profits required to provide for its shipment than can be explained by some objective method of allocating cost and profits. Discrimination may occur at the product level or national level, the latter case occuring if all items from a specific country incur unjustifiably high freight rates. Charges of discrimination have often been based on observed large freight rate differences between two or more countries. However, as Fashbender and Wagner (1973) and UNCTAD (1969) show, in order to prove discrimination exists it is necessary to show that the frieght differences do not reflect differences in the actual cost-of-carriage.
# Table 2a: Ethiopia:

Firm Count by Industry <!-- Start of picture text --> Number of Firms Number of Firms<br>1996 2016<br>Tobacco 1 Petroleum 1<br>Elec. Machinery 1 Recycling 1<br>Tobacco 2<br>Basic Metal 3 Precision Instr. 3<br>Paper 5 Media Equip. 10<br>Vehicles 11 Vehicles 10<br>Machinery 14 Elec. Machinery 12<br>Rubber & Plastics 15 MachineryPaper 2024<br>Garments 23 Basic Metal 63<br>Wood, not furn 26 Wood, not furn 64<br>Textiles 32 Leather 66<br>Chemicals 89<br>Chemicals 35<br>Garments 105<br>Printing 38 Printing 107<br>Fab. metal 39 Fab. metal 174<br>Leather 63 Rubber & Plastics 186<br>Furniture 75 Textiles 258<br>Furniture 395<br>Non-metallic metal 82 Non-metallic metal 533<br>Food & Bev 160 Food & Bev 668<br>0 100 200 300 400 500 600 700 0 100 200 300 400 500 600 700<br>Graphs by Year Graphs by Year<br><!-- End of picture text --> Table 3a: Ethiopia: <!-- Start of picture text --> Firm Shares by Industry<br>Share of Firms Share of Firms<br>1996 2016<br>Tobacco 0.2 Petroleum 0.0<br>Elec. Machinery 0.2 Recycling 0.0<br>Tobacco 0.1<br>Basic Metal 0.5 Precision Instr. 0.1<br>Paper 0.8 Media Equip. 0.4<br>Vehicles 1.8 Vehicles 0.4<br>Machinery 2.2 Elec. Machinery 0.4<br>Rubber & Plastics 2.4 Machinery 0.7<br>Paper 0.9<br>Garments 3.7 Basic Metal 2.3<br>Wood, not furn 4.2 Wood, not furn 2.3<br>Textiles 5.1 Leather 2.4<br>Chemicals 3.2<br>Chemicals 5.6<br>Garments 3.8<br>Printing 6.1 Printing 3.8<br>Fab. metal 6.3 Fab. metal 6.2<br>Leather 10.1 Rubber & Plastics 6.7<br>Furniture 12.0 Textiles 9.2<br>Furniture 14.2<br>Non-metallic metal 13.2 Non-metallic metal 19.1<br>Food & Bev 25.7 Food & Bev 23.9<br>0 5 10 15 20 25 30 0 5 10 15 20 25 30<br>Graphs by Year Graphs by Year<br><!-- End of picture text --> 39
16 Computing growth rates using international prices answers a different question. For ICP-computed growth rates, the questions is "What<sup>is the change</sup> in the value of the goods produced<sup>in a country where value is computed using</sup> the same international price index for all countries?" "Such<sup>growth rates</sup> have the merit of treating a given<sup>increase in a given good as making the same</sup> contribution to growth in both countries.<sup>They have the drawback that the</sup> prices used may be very dissimilar<sup>from the prices of one or both of the</sup> situations." (Kravis and Lipsey, 1990,<sup>p.32)</sup> Thus, we tend to agree with Kravis and<sup>Lipsey (1990) that ICP growth</sup> rates may better capture notions<sup>of international</sup> opportunity costs while own currency growth rates better capture<sup>welfare considerations</sup> because own currency prices more accurately reflect<sup>the representative</sup> consumption baskets of consumers. Statistically, we found that over the period 1960<sup>to 1985 the</sup> two growth rates have a simple correlation<sup>coefficient of almost 0.9.</sup> Turning to economic ratios, there<sup>exist important conceptual and</sup> statistical differences in the use<sup>of international</sup> vs. own currency prices in computing the shares of various economic<sup>variables in GDP. Investment goods</sup> tend to be relatively more expensive<sup>than consumption or government services</sup> in low-income countries, while<sup>government services are generally more</sup> expensive in high-income countries.<sup>This implies that the actual quantity of</sup> investment goods purchased will be<sup>smaller in a low-income than a high-income</sup> country for any given proportion of<sup>income spent on investment. Thus,</sup> investment measured using international prices may more accurately measure<sup>the</sup> augmentation of the capital stock.<sup>One problem with using ICP data in</sup> computing iwvestment shares, however,<sup>is that international</sup> differences in these prices may also reflect trade<sup>and fiscal policies which may</sup>
from the highest to the lowest stratum, and describes, in general terms, the occupations included in each stratum.<sup>44</sup>

||**Table 4. Occupational strata scheme**|
|---|---|
|Stratum|Description|
||Professionals, officials and directors of the public, private and social<br>sector; technicians, educational workers, art, workers in entertainment and|
|Non Manual (NM)|sports/athletics industries, department managers, coordinators and<br>supervisors of administrative activities and services; support workers in<br>administrative activities|
|Commerce<br>High skilled manual (HM)|Employers and employees of established commerce<br>Manual work employer (non-farmers); bosses, supervisors and other<br>control workers in artisanal and industrial manufacturing and in repair and<br>maintenance activities (except for construction workers); operators of<br>fixed machinery in continuous movement and equipment in the industrial<br>manufacturing process (except for operators of specialized mobile<br>equipment for construction); drivers and assistant drivers of mobile<br>machinery or transport vehicles (except for transportation powered by<br>human or animal traction); firemen, policemen and workers of the armed<br>forces.|
||Assistants, day laborers and similar occupations in the artisanal and|
|Low skilled manual<br>handcraft and industrial<br>manufacture (LMM)|industrial manufacturing process or in repair and maintenance activities;<br>construction, installation, finishing and maintenance workers for buildings<br>and other construction activities; operators of specialized mobile<br>equipment for construction.|
|Low skilled manual<br>services (LMS)<br>Agriculture|Street vendors and street service workers; personal services workers in<br>establishments; domestic service workers; watchmen and guards, drivers<br>of transportation powered by means of human or animal traction.<br>Workers in the agricultural sector|

Source: Own elaboration.

Table 5 presents the total income, average schooling and ISEI average of individuals between 30 and 64 years of age belonging to each occupational stratum, according to the income data from the ENIGH 2016.<sup>45</sup> Based on these data, it is possible to affirm that the proposed order is related

> 44 Occupational classifications commonly assign individuals to an occupational stratum through the combination of three variables: occupation, position in the occupation and size of the establishment. Unfortunately, ENCEL 2017 only contained information on the occupation for youths and providers, so the assignment of occupational strata was based on this variable. This restrains the construction of the variable, however, in the case of this sector of the national population the variability of the other two variables is limited, since most are in a wage-earning position and work at small establishments. We consider that, in the absence of the other variables, occupation provides a proper approach to the relevant stratum.

> 45 As previously mentioned, this age range is considered to include adults with a stable occupation and job position.

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here is to determine whether either transport mode offers distinct cost advantages that should be further exploited in future export ventures.

In this study empirical information on the Caribbean countries transportation costs are drawn from two different sources: detailed records maintained by the United States Federal Maritime Commission (FMC) on liner conference freight rates for U.S. export and import trade; and from official United States import statistics. Since 1974 the United States has tabulated imports, by-p7oduct-by-country, on a joint free-alongside-ship (f.a.s.) and cost-insurance-freight (c.i.f.) basis. According to the U.S. practice, the f.a.s. valuation includes the purchase price of the product plus all charges incurred in placing merchandise along side the vessel at the port of exportation. In contrast, the c.i.f. valuation measures the value of imports at the port of entry in the United States and includes all freight, insurance, and other charges (excluding import duties) incurred in bringing the merchandise from the port of exportation and placing it alongside the vessel. As such, the ratio of the c.i.f. to the f.a.s. value provides a measure of the ad valorem incidence of international transport and insurance charges. These data can be used to estimate ad valorem freight and insurance costs for exports of product i from country j (fij) from: where VC represents the c.i.f. and Vf the f.a.s. value of exports of product i from country j.. 6/

> 6/ United States import statistics tabulate the f.a.s. and c.i.f. values of goods imported by air and by ocean vessel. As a result, nominal freight rates for both these modes of transport can be estimated separately. Since equation (1) places the f.a.s. value of imports in the denominator, the nominal freight rates can be compared directly with U.S. tariffs, which are levied on a f.a.s. basis. A further point to note is that equation (1) likely will not reflect port costs as it primarily measures "at sea" charges. Several related studies suggest that port costs and
∆Ycpt = γ∆FDI_shock_outputcpt + θFDI_outputsharecpt−1 + θYcpt−1 + Xcpt−1′ β+ αpt + εcpt Results in Table A4 confirms that measuring FDI presence with output share does not affect our findings.

||TableA4.Me<br>|asuringFDIwit <br>|houtput share<br>|||
|---|---|---|---|---|---|
||(1)|(2)|(3)|(4)|(5)|
|||Panel A<br>||Pane<br>|l B<br>|
|Dependent var|∆agri_emps|∆indu_emps|∆serv_emps|∆Highschool|∆College|
|∆FDI_shock_output|-1.929**|0.452|1.077***|0.975***|0.502***|
||(0.753)|(0.499)|(0.341)|(0.293)|(0.112)|
|L.FDI_outputshare|-0.0510|-0.0186|0.0817**|-0.0729***|0.0796***|
||(0.0326)|(0.0455)|(0.0395)|(0.0229)|(0.0223)|
|Observations|609|609|609|609|609|
|R-squared|0.554|0.567|0.513|0.821|0.752|
|Prov-Year FE|Y|Y|Y|Y|Y|
|Lagged city characteristics|Y|Y|Y|Y|Y|
|SOE reform|Y|Y|Y|Y|Y|
|Tariff shocks|Y|Y|Y|Y|Y|
|Excludingmigrants|Y|Y|Y|Y|Y|

Note: This table shows the effects of FDI shock on city level structural transformation and school enrollment using alternative FDI measure. Standard errors are clustered at the city level. *** p<0.01, ** p<0.05, * p<0.1.

Appendix 3. Heterogenous effects on school enrollment by FDI type, age group and gender

|TableA5.|FDI inflows andloc<br>|alschoolenro<br>|llment: by age gro<br>|up andFDIe<br>|xportintensity<br>||
|---|---|---|---|---|---|---|
||(1)|(2)|(3)|(4)|(5)|(6)|
||Panel A:|16-18|Panel B:|19-22|Panel C:|23-25|
|Dependent var|∆Highschool|∆College|∆Highschool|∆College|∆Highschool|∆College|
|∆FDI_export_H|2.315***|-0.100|0.693|0.732|0.879**|0.672*|
||(0.671)|(0.182)|(0.437)|(0.457)|(0.351)|(0.361)|
|∆FDI_export_L|1.918**|0.0855|1.355**|0.857**|1.567***|1.407***|
||(0.837)|(0.138)|(0.587)|(0.419)|(0.458)|(0.399)|
|Observations|609|609|609|609|609|609|
|R-squared|0.838|0.495|0.846|0.674|0.721|0.589|
|Prov-Year FE|Y|Y|Y|Y|Y|Y|
|Lagged city|||||||
|characteristics|Y|Y|Y|Y|Y|Y|
|SOE reform|Y|Y|Y|Y|Y|Y|
|Tariff shocks|Y|Y|Y|Y|Y|Y|
|Excludingmigrants|Y|Y|Y|Y|Y|Y|

Note: This table reports the effect of FDI shock on city level school enrollment by student age group and FDI export intensity. Standard errors are clustered at the city level. *** p<0.01, ** p<0.05, * p<0.1.

38
# **Redressing Grievances and Complaints Regarding Basic Service Delivery**

Varun Gauri<sup></sup>

**JEL Codes:** I10 (Health), I21 (Analysis of Education), I38 (Welfare and Poverty, Government Policy), K23 (Regulated Industries and Administrative Law)

**Keywords:** Basic services, governance, administrative law

>  Development Research Group, The World Bank. Sheila Myung provided valuable research assistance for this paper; Ariel Fiszbein, Dena Ringold, and Chris Stolk gave very helpful comments; and Colin Andrews, Edmundo Murrugurra, and Francisco Ochoa provided useful inputs and documentation. The views expressed here are those of the author alone and do not necessarily represent the opinions of the World Bank or its Executive Directors.
Table 5: Spillovers: Literacy and Numeracy Skills of Siblings (6-12 year olds)

|||_Literacy_|_Skills_||_Numer_|_acy Skills_|
|---|---|---|---|---|---|---|
||Prop.|Zero|Z-s|core|Z-|score|
||(1)|(2)|(3)|(4)|(5)|(6)|
|Any Treatment (T1 or T2)|-0.050<br>(0.038)||0.064<br>(0.116)||0.092<br>(0.122)||
|Videos (T1)||0.032||-0.190*||-0.257**|
|||(0.046)||(0.115)||(0.123)|
|Videos + Mobile apps (T2)||-0.138***<br>(0.039)||0.340**<br>(0.145)||0.471***<br>(0.141)|
|Observations|5,268|5,268|5,268|5,268|5,268|5,268|
|R-squared|0.117|0.130|0.198|0.204|0.227|0.243|
|Covariates|YES|YES|YES|YES|YES|YES|
|Enumerator FE|YES|YES|YES|YES|YES|YES|
|Baseline Mean|0.942|0.942|0.000|0.000|0.000|0.000|
|Control Mean in F-up|0.476|0.476|0.932|0.932|1.127|1.127|
|T1 vs T2(p-value)||0.000||0.000||0.000|

Note: Siblings’ age is defined according to their age at baseline. Column 1 and 2 show the results of linear probability models of the proportion of children scoring ”zero” in letter recognition. Column 3-4 show z-scores of literacy skills and Column 5-6 z- scores of skills. Test scores are normalized the mean and standard numeracy using deviation for the control at baseline. All have clustered standard group regressions errors at the school catchment area level. The covariates considered in all regressions are: age of target child, gender of target child, education of household head (baseline), availability of food (baseline) and enumerator fixed effects. *** p _<_ 0.01, ** p _<_ 0.05, * p _<_ 0.1 When we separately identify the households that received the mobile phone with m- Learning apps and those who did not receive the device living in _T_ 2 communities, we observe large spillovers on target children that did not receive the device. Table 6 shows that treatment effects on foundational skills (column 2) and numeracy (column 6) are larger for target children receiving the device. Moreover, the effects on literacy and numeracy skills are significant for target children not receiving the device. Treatment effects on foundational literacy and numeracy skills on target children without mobile phone, living in _T_ 2 communities, are approximately 75% as large as the effect observed on target children directly benefiting from the mobile phone with m-Learning apps. When analyzing siblings within _T_ 2 communities, the spillovers effects are as large as those observed on siblings living in households that received the mobile phone, see Table A15.

29