rafmacalaba/datause-encoder-data huggingface dataset:
that the confidence intervals do not reflect the uncertainty regarding the estimation of _rpj_ , which is usually constructed based on a small set of items. Thus, we cannot reject the common working assumption that the dynamics of the price of ICT relative to NICT are uniform across countries at different levels of development. Moreover, it is perhaps worth noting that we show price dynamics between 2005 and 2010 here—a period in which relative ICT prices weren’t falling dramatically even in the U.S. (see Figure 2). Thus, our finding of no systematic worldwide decline in ICT prices between 2005 and 2010 is perfectly consistent with the assertion that the US price trend is relevant more globally.

In sum, these findings suggests a simple strategy to translate the nominal shares reported in panel A of Figure 1 and Table 2 into a measure of ICT units per NICT unit that is comparable across countries. Specifically, we model country and time specific ICT and NICT prices as where _p_ ( _i, k, t_ ) is some relative price of capital in country _i_ at time _t_ , which may vary over time. _p_ ( _ICT, i_ ) and _p_ ( _NICT, i_ ) are country-specific ICT and non-ICT components that are time invariant. Given these definitions, the physical units of ICT per unit of NICT capital can be written as _<u>p</u>_ <u>(</u> _ICT,i,t_ <u>)</u> _·K_ <u>(</u> _ICT,i,t_ <u>)</u> Notice that _p_ ( _NICT,i,t_ ) _·K_ ( _NICT,i,t_ )<sup>is the nominal ratio reported in panel A of Figure 1 and Table 2,</sup> _K_ <u>(</u> _ICT_ <u>)</u> and we can therefore easily translate it into the real ratio _K_ ( _NICT_ )<sup>by multiplying it with</sup> where we proxy the first fraction with our US relative price measures based on the BEA (Eden and Gaggl, 2014) and the second fraction using our country specific relative price based on equation 15
**Table 4:** Average employment effects

**Panel A: After one** **<u>year</u>**

||For|mal|Perm|anent|Temp|orary|Info|rmal|
|---|---|---|---|---|---|---|---|---|
||(1)|(2)|(3)|(4)|(5)|(6)|(7)|(8)|
|Incentive|0.042<sup>_∗∗_</sup>|0.008|0.051<sup>_∗∗∗_</sup>|0.005|-0.009|0.004|-0.019|0.046|
||(0.017)|(0.025)|(0.015)|(0.018)|(0.011)|(0.019)|(0.025)|(0.053)|
|Observations|18468|4620|18468|4620|18468|4620|14040|3252|
|R square|0.11|0.05|0.09|0.04|0.03|0.05|0.06|0.14|
|Control Mean|0.25|0.12|0.17|0.06|0.08|0.06|0.39|0.54|
|Std Dev|0.43|0.43|0.37|0.37|0.26|0.26|0.49|0.49|
|Sample|Techn.|Gener.|Techn.|Gener.|Techn.|Gener.|Techn.|Gener.|

**Panel B: After two years**

||For|mal|Perm|anent|Temp|orary|Info|rmal|
|---|---|---|---|---|---|---|---|---|
||(1)|(2)|(3)|(4)|(5)|(6)|(7)|(8)|
|Incentive|0.042<sup>_∗∗_</sup>|-0.010|0.050<sup>_∗∗∗_</sup>|-0.005|-0.008|-0.005|-0.013|0.053|
||(0.017)|(0.027)|(0.016)|(0.020)|(0.011)|(0.021)|(0.025)|(0.051)|
|Observations|36936|9240|36936|9240|36936|9240|28080|6504|
|R square|0.10|0.06|0.09|0.04|0.02|0.06|0.05|0.14|
|Control Mean|0.29|0.16|0.20|0.08|0.09|0.07|0.39|0.54|
|Std Dev|0.45|0.45|0.40|0.40|0.28|0.28|0.49|0.49|
|Sample|Techn.|Gener.|Techn.|Gener.|Techn.|Gener.|Techn.|Gener.|

_Notes:_ Source: IMSS administrative data for formal employment and endline survey data for informal employment, June 2019-May 2021. Observations are at the person-month level. Standard errors, clustered at individual level, are reported in parentheses. We control for demographic characteristics, socioeconomic status, work experience, time and school fixed effects.<sup>_∗_</sup> _p <_ 0 _._ 10,<sup>_∗∗_</sup> _p <_ 0 _._ 05,<sup>_∗∗∗_</sup> _p <_ 0 _._ 01 and 24 months, respectively (Col. 3). The share with temporary contracts is not affected (Col. 5). Results are very similar in magnitude and significance when we do not control for any covariates (Appendix Table A2). By contrast, we do not observe a change in formal employment or switching between employment types for graduates from general schools (Col. 2, 4, 6) or those planning to continue their education. The fact that the wage incentive does not change decisions to enter the labor market for this group assuages concerns about unintended consequences of offering short-term monetary incentives for formal work.

Our results further suggest that slightly less than half of these average formal employment gains come from a reduction in informal work (Panel A and B, Col. 7). One potential concern with the analysis of informal employment is that we rely on self-reported data from our endline survey. To test the reliability of this data, we take advantage of the fact that 21
15

# Tabh la

|Cross-Cou<br>Period|ntry Ave|rages; /All Countrie<br>Early|s: for 1965<br>L|-73 and 1974-85<br>ate|
|---|---|---|---|---|
||Mean|Std Dev|Mean|Std Dev|
|GDP growth|.|,.03|.04|.03|
|Real Int Rate|-.02|.07|-.05|.08|
|IncOut/CapRatio|.32|.13|.14|.12|
|Invest Rate|.21|.06|.24|.08|
|Inflation|.18|.34|.34|.47|
|M3/GDP|.28|.15|.35|.18|
|RFSTS<sup>1 8</sup>|.14|.10|.07|.13|
|Volatility<sup>1 9</sup>|.13|.28|.16|.23|

|Cr|oss-Country Averages for O<br>High Growth Countries<br>Mean<br>Std Dev|verall Period<br>Low Growth<br>Mean|1965-85<br>Countries<br>Std Dev|
|---|---|---|---|
|GDP growth|.06<br>.02|.03|.01|
|Real Int Rate|-.01<br>.04|-.07|.06|
|IncOut/CapRatio|.27<br>.08|.16|.07|
|Invest Rate|.26<br>.07|.19|.05|
|Inflation|.20<br>.23|.34|.41|
|M3/GDP|.39<br>.20|.26|.07|
|RFSTS|.15<br>.09|.05|.07|
|Volatility|.10<br>.16|.19|.21|

18 RFSTS measures the extent to which savings flows are reflected in increases in real broad money, and thus the extent to which they can be said to be "financialized." It is defined below.

19 Volatility is measured by the absolute deviation of inflation rates from their previous year's lev3l.
|**May/June 2013Data**||rank||
|---|---|---|---|
|type_of|obs|sum|expected|
|Traditional|83|7292|6640|
|mirt|76|5428|6080|
|Combined|159|12720|12720|
|unadjusted variance<br>adjustment for ties|84106.67<br>0|||
|adjusted variance|84106.67|||

Ho:SC(type_of==Traditional) =SC(type_of==mirt) z = 2.248 Prob> z = 0.0246

# **Specific fuel consumption in** **<u>Amhara</u>**

|**Amhara Region Data**<br>type_of|obs|rank<br>sum|expected|
|---|---|---|---|
|Traditional|46|2600|2093|
|mirt|44|1495|2002|
|Combined|90|4095|4095|
|unadjusted variance<br>adjustment for ties|15348.67<br>0|||
|adjusted variance|15348.67|||

|Ho:SC(type_of==Traditional) =SC(type_of==mirt)<br>z = 4.092|
|---|
|Prob>|z|=0.0000|

38 | P a g e
# **3. Data**

This section describes the data used in our analysis. We combine the official test and school databases and the investment data that we collected in the (TEEP) divisions. For test scores and school conditions at the start of the project, we use the National Achievement Test (NAT) score data and the Basic Education Information System (BEIS) data, respectively.

The NAT data provide average test scores for grade 4 students in school year (SY) 2002/03, grade 5 in SY 2003/04, and grade 6 in SY 2004/05 for each school. We note that grade 4 in SY 2002/03, grade 5 in SY 2003/04, and grade 6 in SY 2004/05 constitute panel data that tracked the same cohort in each school.<sup>10</sup> Double differences (DD) based on the cohort panel from grade 4 (SY 2002/03) and grade 6 (SY 2004/05) is used to eliminate cohortspecific fixed effects.<sup>11</sup> Table 3.1 shows the mean and standard deviation of mathematics and overall scores of the cohort in SY 2002/03 and SY 2004/05 for TEEP and non-TEEP areas, separately. TEEP schools have significantly higher average scores than non-TEEP schools in both years. The BEIS data provide detailed information on student enrollment and achievements and teachers since SY 2002/03. The data normally disaggregate the information by grade, age, and gender.<sup>12</sup> Since BEIS was established as part of TEEP, we do not have systematically

> 10 National achievement test is self-administered at schools, which potentially creates bias in raw test scores. For grade-6 students, tests were implemented in each year.  In our analysis, however, we use an experimental introduction of the test for the same cohort: Grade 4 in SY 2002/03 and Grade 6 in SY 2004/05.

> 11 Due to delayed preparations at the early stage of TEEP, most of the program schools received investments after SY 2002/03.

> 12 BEIS data needed intensive programming to transform for analysis. The data were originally in Microsoft Excel. The computer program needed about 10 hours to reorganize school-level data in different divisions and regions for one school year.

8
|T|able 11: List of Variables|
|---|---|
|**Child Characteristics**||
|AGE|-<br>age of child|
|AGESQ|- age of child squared|
|FEMALE|- gender (female = 1)|
|**Parent Characteristics**<br>||
|EDUCFA|- years of education of father|
|EDUCFA X FEMALE|- years of education of father X gender of child|
|EDUCMO|- years of education of mother|
|EDUCMO X FEMALE<br>EMPFA|- years of education of mother X gender of child<br>- father employed|
|EMPFA X FEMALE<br>|- father employed X gender of child<br>|
|EMPMO|- mother employed|
|EMPMO X FEMALE|-motheremployed<br>X gender of child|
|**Household Characteristics**||
|HEADAGE|-<br>age of head|
|HEADAGESQ|-<br>age of head squared|
|HEADFEMALE<br>|<br>-<br>gender of head (female = 1)<br>|
|#BOYS 0-5|- # of other boys in household 0-5 years|
|#BOYS 6-9|- # of other boys in household 6-9 years|
|#BOYS 10-15|- # of other boys in household 10-15 years|
|#BOYS 16-17|- # of other boys in household 16-17 years|
|#GIRLS 0-5|-<br># of other girls 0-5 years|
|#GIRLS 6-9|-<br># of other girls 6-9 years|
|#GIRLS 10-15|- # of other girls 10-15 years|
|#GIRLS 16-17|<br>- # of other girls 16-17 years|
|FARM|- household owns farm|
|BUSINESS|- household owns non-farm enterprise|
|POOR|- household in poorest quintile|
|**Cost of Schooling**||
|COST|- cluster average of household education expenditure<br>per pupil ('000 CFAF)|
||-<br>school less than 1 km away (omitted)|
|DISTANCE 1-5|-<br>school 1-5 km away|
|DISTANCE 5+|- school >5 km away|
|**Location (urban)**||
||**-**<br>Abidjan (omitted)|
|OTHERCITIES|<br>-<br>other cities|
|**Location (rural)**||
||**-**<br>East Forest (omitted)|
|WFOREST|- West Forest|
|SAVANNAH|- Savannah|

39
# **T'able 2. The Benchmark Results of PC Effects**

# **(Dep.=in(value added per employee) ): Controlling for Other Reforms**

|Sample:|Pooled (|Participant|s & Non-Participants||PC-particip|ants Only|
|---|---|---|---|---|---|---|
||OLS (1)|FE (2)|First Difference<br>GMM|OLS|FE|First Difference<br>GMM|
|Dummy: PC Participants (that is di=l dummy)|.108<br>(.098)||||||
|Preceding dummy*number of years in the<br>sample|.008<br>(.019)|.004<br>(.010)|.033<br>(.029)||||
|Dummy: the year before PC-adoption (Dt-.1)|.018<br>(.053)|-.010<br>(.039)||.027<br>(.055)|-.037<br>(.039)||
|**Dummy: post-PCit (that is, Dit)**|.034<br>(.069)|-.020<br>(.051)|.002<br>(.038)|.023<br>(.071)|-.058<br>(.052)|-.018<br>(.038)|
|**Post-PCit***the number of years since<br>**the adoption (i.e., Dit*Tit)**|.037*<br>(.020)|.034*<br>(.020)|.022<br>(.036)|.033*<br>(.019)|-.003<br>(.020)|.034<br>(.038)|
|Number of observations|6176|6176|3673|5471|5471|3253|
|R square|.412|.455||.414|.490||
|N*objective|||64.79|||58.27|

Note. (a) Standard errors (OLS and 2SLS estimates has corrected for heteroskedasticity) in parentheses. *, **, and *** represents significance levels of 10, 5, and I percent. (b) R squares reported under FE models are R squares within. (c) In all the contests between OLS, RE, and FE specifications, the FE models win decisively. (d) Other control variables for all specifications include year dummies of 1984 to 1989; other reforms: marginal profit retention rate and its missing indicator, the dummies of managerial wage discretion, of production autonomy, of the presence of new management; the coefficients for capital-labor ratios and the numbers of employees for different industry-sizes are reported below. Rows under "OLS, pooled" relate to OLS(l) above, and "FE, pooled" FE(2) above: _35_
(1974)). Revenue windfalls appear to represent loosenings **in** these key macroeconomic and political constraints. Tanxi (1982) cites limited expenditure control **a3** an important factor contributing to fiscal imbalances in Cote d'Ivoire, Kenya, Malawi, Sudan, Zaire and Zambia.

For the same reasons, reducing expenditures when revenues fall has proven extremely difficult.<sup>15</sup> Countries that have handled commodity booms well have tended to follow policies aimed at building wealth and liquidity, thereby . towing increases in current consumption without tightening future constraints. For many countries, however, the tendency for expenditures to expand rapidly with revenue booms **and** to contract only slowly with revenue declines has meant a tendency for deficits to -atchet upwards over time (cf. 'Wheeler, 1984). This process can continue as long **as** external financing is available; its costs become apparent in the contractionary adjustment that is ultimately required.

If the duration of commodity price fluctuations were known, economic theory would indicate increasing assets during booms and depleting them during bust periods. Only permanent movements in real commodity prices would change the country's real wealth and therefore indicate changes in the sustainable standard of living. The actual country experience, however (e.g., Kenya and Madagascar during the 1976-77 coffee boom, Cote d'Ivoire and Ghana during the 1975-77 cocoa **and** coffee boom, Zambia during the sustained copper boom of 1965-74), is full of examples where windfall revenue gains led to large 15 In a caLeful study of stabilization programs (including Ghana, Kenya and Zambia, as well as countries outside of Africa) Nelson (1974) concludes that governments find cutting deficits to be the most difficult part of the programs.

13
**Table 2. Variable names, definitions and components**

|**lint_usage**|||**Log digital whole economy index (PCA)**|
|---|---|---|---|
|||I99H|Percentage of individuals using the Internet|
|||XHH4_IDI|Percentage of households with computer|
|||XHH6_IDI|Percentage of households with Internet|
|**ldig_bus_pca**|||**Log digital business index (PCA)**|
|||fix_broad_sus|Fixed broadband subscriptions (per 100 people)|
|||sec_int_servers|Secure Internet servers (per 1 million people)|
|**lgdppcppp**|||**Log of GDP PC PPP**|
|**lwdi_inflation**|||**Log of inflation rate**|
|**lopeness**|||**Log of openness**|
|**linf_index**|||**Log of physical infrastructure index**|
|**lmwtova_pw**|||**Log MW to VA per worker**|
|**lrest_oecd**|||**Log OECD measure of regulation (epl)**|
|**lrest_ilo**|||**Log ILO measure of regulation (eplex)**|
|**lepl_db**|||**Log of EPL - DB index (PCA)**|
||**dif_hir_idx**||**Log Difficulty of hiring index (PCA)**|
|||v01|Are fixed-term contracts only allowed for fixed-term tasks?<br>What is the maximum duration of fixed-term contracts (in|
|||v02|months)?|
||**rig_hours**||**Log Rigidity of hours index (PCA)**|
|||v05|50 hour weeks for 2 months?|
|||v06|(5j-) Are there restrictions on night work?|
|||v07|(5k-) Are there restrictions on 'weekly holiday' work?|
|||v08|(5f-) What is the maximum number of working days per week?|
||**dif_firing_idx**||**Log Difficulty of firing index (PCA)**<br>Must the employer notify a third party before dismissing one|
|||v12|<br>redundant employee?|
||||<br>Does the employer need the approval of a third party to dismiss|
|||v13|one redundant worker?|
|||v14|Must the employer notify a third party prior to dismissing 9 or<br>more redundant workers?|
||||Does the employer need the approval of a third party prior to a|
|||v15|<br>collective dismissal|
||||Does the law mandate retraining or reassignment prior to|
|||v16|dismissal?|
|||v17|Are there priority rules applying to dismissal or lay-offs?|
|||v18|Are there priority rules applying to re-employment?|
||**firing_cost_idx**||**Log Firing cost index (PCA)**|
|||v34|Paid annual leave (working days) - 10 years<br>|
||||Notice period for redundancy dismissal after 10 years of|
|||v37|<br>continuous employment|
||||Severance pay for redundancy dismissal after 10 years of|
|||v40|<br>continuous employment|
||||<br>Log of Old age, disability, and survivors’ pension contribution,|
|**loads_emp**|||employer|
||||Log of Old age, disability, and survivors’ pension contribution,|
|**loads_ins**|||insured worker|
|**lossp_emp**|||Log of All social insurance programs contribution, employer|
|**lossp_ins**|||Logof All social insuranceprograms contribution,insured worker|

8
67 Table D.6: **Resources, Industrialization & Growth of FUAs, 1975-2015, Cross-Section**

|**Dependent Variable**||L|og FUA P|op. 2015|- Log FU|A Pop. 19|75||
|---|---|---|---|---|---|---|---|---|
|Capital + Largest City:|Top 1|Top 2|Top 5|Top 0|Top 1|Top 2|Top 5|Top 0|
||(1)|(2)|(3)|(4)|(5)|(6)|(7)|(8)|
|MFGSERV*TOP|-0.02|-0.01|0.00|-0.01|-0.02|-0.01|0.00|-0.01|
||[0.025]|[0.024]|[0.025]|[0.025]|[0.026]|[0.025]|[0.026]|[0.026]|
|NRXGDP*TOP|-0.01|-0.01|-0.01|-0.00|||||
||[0.029]|[0.023]|[0.018]|[0.039]|||||
|DEINDU*TOP|0.04|0.03|0.01|0.05|0.04|0.03|0.01|0.05|
||[0.047]|[0.046]|[0.044]|[0.051]|[0.047]|[0.046]|[0.044]|[0.051]|
|FMXGDP*TOP|||||-0.01|-0.01|-0.01|-0.00|
||||||[0.031]|[0.024]|[0.019]|[0.042]|
|AGXGDP*TOP|||||-0.06|-0.04|-0.05|-0.05|
||||||[0.057]|[0.052]|[0.046]|[0.061]|
|TOP*(NRXGDP - MFGSERV)|0.00|0.00|-0.01|0.00|||||
||[0.05]|[0.04]|[0.04]|[0.06]|||||
|TOP*(DEINDU - MFGSERV)|0.06|0.04|0.01|0.06|0.06|0.04|0.01|0.06|
||[0.07]|[0.06]|[0.06]|[0.07]|[0.07]|[0.07]|[0.06]|[0.07]|
|TOP*(FMXGDP - MFGSERV)|||||0.01|0.00|-0.01|0.01|
||||||[0.05]|[0.04]|[0.04]|[0.06]|
|TOP*(AGXGDP - MFGSERV)|||||-0.04|-0.03|-0.05|-0.04|
||||||[0.05]|[0.05]|[0.04]|[0.05]|
|Observations|7,422|7,422|7,422|7,422|7,422|7,422|7,422|7,422|

_Notes:_ Robust SE in parentheses. * p _<_ 0.10, ** p _<_ 0.05, *** p _<_ 0.01.

# Table D.7: **Resources, Industrialization & Growth of FUAs, 1975-2015, Panel**

|Dependent Variable:|||Lo|g FUA P|op. in Yea|r_t_|||
|---|---|---|---|---|---|---|---|---|
|Extra Lags Included||1 Extr|a Lag|||2 Extr|a Lags||
|Capital + Largest City:|Top 1|Top 2|Top 5|Top 0|Top 1|Top 2|Top 5|Top 0|
||(1)|(2)|(3)|(4)|(5)|(6)|(7)|(8)|
|Sum of Lags for MFGSERV|0.01|0.00|0.00|0.00|0.02**|0.01*|0.01*|0.01|
||[0.01]|[0.01]|[0.01]|[0.01]|[0.01]|[0.01]|[0.01]|[0.01]|
|Sum of Lags for NRXGDP|0.01|0.01|0.01|0.01|0.02|0.02|0.02|0.03|
||[0.02]|[0.01]|[0.01]|[0.02]|[0.02]|[0.02]|[0.02]|[0.02]|
|Sum of Lags for DEINDU|-0.02|-0.01|-0.01|-0.02|-0.01|-0.01|-0.00|-0.02|
||[0.02]|[0.02]|[0.02]|[0.01]|[0.01]|[0.01]|[0.01]|[0.01]|
|Observations|29,681|29,681|29,681|29,681|22,259|22,259|22,259|22,259|
|City FE|Y|Y|Y|Y|Y|Y|Y|Y|
|Country-Year FE|Y|Y|Y|Y|Y|Y|Y|Y|

_Notes:_ Robust SE clust. at the city level in parentheses. * p _<_ 0.10, ** p _<_ 0.05, *** p _<_ 0.01.
Third, the model focuses on some standard government rules and our sustainable approach can be improved to take into account a sovereign wealth fund that can serve as a stabilization buffer, enabling households and government to smooth consumption and public investment paths over negative economic shocks. Finally, our analysis suggests that a high proportion of oil revenues should be invested, but does not specify which type of investment should be prioritized. The analysis could be deepened so as to determine how much of the resources should be used to increase soft/hard infrastructure.

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