rafmacalaba/datause-encoder-data huggingface dataset:
14 Besides suggesting that greater financial intermediary development causes faster economic growth, the results indicate an economically large relationship. For example, India's value of PRIVATE CREDIT over the **1960-95** period was **_19.5_** percent of **GDP,** while the mean value for developing countries was **_25_** percent of **GDP.** The results suggest that an exogenous improvement in PRIVATE CREDIT in India that had pushed it to the sample mean for developing countries would have accelerated real per capita **GDP** growth **by** about **0.6** percentage point per year.<sup>9</sup> Similarly, if Argentina had moved from its value of PRIVATE CREDIT **(16)** to the developing country sample mean, it would have grown more than one percentage point faster per year. This is large considering that growth only averaged about **1.8** percent per year over this period.

# **D.** Sensitivity Analyses

We have conducted a number of sensitivity analyses to gauge the robustness of these findings. For instance, the two-stage least squares estimator gives similar results to the GMM estimator reported above. We have also restricted the sample to those countries for which LLSV **(1998)** collect legal data. This did not alter the results. Furthermore, as control variables, we used measures of the efficiency of the bureaucracy and the level of bureaucratic red tape [Knack and Keefer **1995;** Mauro **1995],** as well as measures of terms of trade changes and population growth. These did not alter our findings. We controlled for the level of stock market development (Levine and Zervos **1998)** without changing the conclusion that financial intermediary development exerts a positive impact on long-run growth. We also experimented with two additional measures of financial intermediary development. One measure equals deposit money bank credit to the private sector divided **by GDP.** This is smaller than PRIVATE CREDIT, which also includes other financial intermediaries. The second additional measure equals the

> **9** To get this, recall that the regressors are in logs and note that the ln(25) **-** ln(19.5) **= 0.25.** Then, use the smallest parameter on **PRIVATE CREDIT** from **Table 3,** which equals **2.5,** so that **2.5*(0.25) = 0.63.**
the 69 BAMs (7% of the treatment group). We present these results in the Appendix (Tables A8 and A9). The magnitudes of our estimates are smaller but the effects on our summary outcome measures remain statistically significant. Appendix Figure A1 indicates that monthly incomes for the BAMs were higher than for other public works participants in treatment sites, peaking in the range of 30,000 CFA, which is close to the 37,000 CFA monthly payment for BAMs working in the childcare centers. Figure A2 similarly indicates a rightward shift in the employment index. Finally, Figures A3 and A4 indicate that the income distribution and employment index in the treatment group substantially converge towards those of the control group once we remove the BAMs from our analysis sample, suggesting that the direct employment channel was important in this context, albeit not the only mechanism at work.

# **5 Discussion**

There are certain limitations of the research design worth noting. First, we cannot measure the effects of access to childcare on the decision to participate in the public works program because the childcare intervention was introduced several months after participants had already been recruited and commenced their work assignments. Second, the World Bank project team provided increased monitoring and safeguards to the public works program in sites with childcare centers, so we cannot perfectly isolate the effects of access to childcare from the effects of the additional accompanying measures provided to treatment sites. Third, public works participants in both the treatment and control sites received parental education training from social workers, which could have improved their ability to stimulate their children and could reduce the differences in early childhood development outcomes observed across the two groups. Finally, the COVID-19 pandemic reduced childcare exposure to eight months, instead of one year as planned. There is evidence from nationally representative data collected at the early stages of the COVID-19 pandemic in Burkina Faso that the pandemic has reduced economic opportunities, which may limit the employment and earning effects of the intervention. Our observed impacts are therefore suggestive of potentially larger impacts of sustained childcare provision in a more favorable economic climate. Conversely, follow-up child assessments were conducted by phone (not direct observation) due to social distancing protocols, so there is some potential for bias from self-reports. Altogether, these results provide strong indication 20
|Springel (2019)|Norway|2010-2015|Vehicle registrations,<br>information on the charging<br>station network and<br>government incentives|OLS|the effects of PEV stock on<br>investment in charging<br>stations|
|---|---|---|---|---|---|
|**_Fuel-Economy S_**<br>|**_tandards_**<br>||<br>|||
|Busse et al<br>(2013)|United States|1999-2008|Automobile transactions,<br>characteristics and fuel<br>consumption, station level<br>gasoline prices|Reduced form<br>regressions|Consumers discount future<br>fuel-cost savings|
|Allcott &<br>Wozny (2014)|United States|1999-2008|Average prices, quantities,<br>and characteristics of all<br>used passenger vehicle<br>model, fuel economy data|OLS||
|Sallee et al<br>(2016)|United States|1993-2008|Microdata from wholesale<br>used car auctions (the<br>transaction price,<br>transaction date, odometer<br>reading and truncated<br>Vehicle Identification<br>Number|OLS||
|Goldberg<br>(1998)|United States|1984-1990|Consumer Expenditure<br>Survey (automobile sales,<br>prices, and fuel<br>consumption)|Discrete Choice Model,<br>Demand side model<br>combined with<br>oligopoly and product<br>differentiation models,<br>Simulation|The empirical analysis did<br>not find evidence of any<br>utilization effects (at least<br>not for relatively small<br>changes in fuel costs),<br>while the substitution<br>effects towards used cars<br>were estimated to be small|
|Grigolon et al<br>(2018)|European<br>Countries:<br>Belgium,<br>France,<br>Germany,|1998-2011|Sales, prices, and product<br>characteristics for every<br>new passenger car sold|GMM|Fuel taxes are more<br>effective in reducing fuel<br>usage than product taxes<br>and perform better in terms|

52
The third source is Mexico’s National Standards ( _Estándares Nacionales_ ) exam. These data were collected by Mexico’s Ministry of Education ( _SEP-Secretaría de Educación Publica_ ) through the General Directorate of Evaluation ( _Dirección General de Evaluación_ ). SEP conducted the National Standards exam annually between 1998 and 2002, with different grades and background questionnaires used for each year. The 2001 exam for fifth grade students includes 47,719 students from 3,221 schools and also includes the most extensive background data, hence that is the round of the exam that we use in the present analysis. National Standards was stratified by school type – community courses, indigenous schools, urban public schools, urban private schools and rural schools – and this stratification allows representativeness of each school type in the sample. Background questionnaires were obtained from students taking the exam, from a random sample of fifth grade teachers in the selected sample schools – not necessarily instructors of the students who took the exam – and from principals of the sample schools. National Standards uses the Rasch Correction, a form of IRT scaling, to adjust scores from raw to final values (further description of the National Standards data appears in Shapiro and Moreno 2004).

Officially, Mexico has five types of schools: Communitarian Schools, Public Rural Schools, Public Urban Schools, Private Urban Schools and Indigenous Schools. In this analysis the type of school was the only variable that allowed to identify the indigenous students, however, in Mexico the indigenous population that attends school is not only concentrated in the Indigenous Schools and many of these people go to others schools. So, this measure is certainly under-represented.

12
# **4.5  Sensitivity analysis**

Results from a CGE model are sensitive to many parameters. The most critical parameters are the ”elasticities of substitution,” which are measures of how flexibly producers can respond to changes in relative prices, including a carbon tax and cuts in other taxes. We carried out two sets of sensitivity analyses for all scenarios. In the first set of sensitivity analyses, we decreased the value of all elasticity parameters by 25%. In the second set of sensitivity analyses, we increased the values of elasticity parameters by 25%.

The results of the sensitivity analyses are presented in Table 9. The results confirm that the findings of the study are robust. There is no change in the direction of results, and the magnitudes of the changes are as expected. When the values of elasticity parameters are increased, both economic impacts and emission impacts improve: GDP and welfare become less negative if they are negative in the main analysis and more positive if they are positive in the main analysis. Similarly, the increase in elasticity values increases emission reductions. The economic and emissions reduction impacts of decreased elasticities of substitution are the opposite of those under increased elasticity values.

**Table 9. Results of sensitivity analysis on key variables along with that in the main analysis (% change from the base case)**

|Scenario|Main Case|Sensitivit|y case|Main Case|Se|nsitivity case|
|---|---|---|---|---|---|---|
|||Elasticity<br>increased|Elasticity<br>decreased||Elasticity<br>increased|Elasticity<br>decreased|
|||GDP|||Welfare||
|Scenario 1|-0.028|-0.025|-0.032|-0.038|-0.034|-0.041|
|Scenario 2a|-0.072|-0.069|-0.075|-0.062|-0.060|-0.065|
|Scenario 3a|0.103|0.108|0.097|0.089|0.093|0.084|
|Scenario 4c|0.121|0.123|0.118|0.082|0.084|0.080|
|||S<br>|ectoral output||||
|||Formal sector||In|formal sector||
|Scenario 1|-1.477|-1.609|-1.343|1.184|1.234|1.132|
|Scenario 2a|-0.512|-0.609|-0.413|0.112|0.160|0.062|
|Scenario 3a|-0.853|-0.987|-0.716|0.877|0.964|0.784|
|Scenario 4a|-0.913|-1.031|-0.792|1.254|1.304|1.202|
|||C|O2 emissions||||
|Scenario1|-10.119|-11.257|-8.949|-3.541|-4.486|-2.598|
|Scenario 2a|-4.741|-5.611|-3.853|-4.627|-5.380|-3.878|
|Scenario 3a|-7.977|-9.368|-6.527|-3.735|-4.492|-2.997|
|Scenario4c|-9.699|-10.739|-8.633|-1.809|-2.524|-1.106|

33
# **Table 4: Returns to education:  treatment and interaction effects in OLS**

|||Men<br>|||Women<br>||
|---|---|---|---|---|---|---|
||(1)<br>ln(w)|(2)<br>ln(w)|(3)<br>ln(w)|(4)<br>ln(w)|(5)<br>ln(w)|(6)<br>ln(w)|
|Years of schooling|0.08***|0.08***|0.04**|0.10***|0.10***|0.08**|
||(0.02)|(0.02)|(0.02)|(0.03)|(0.03)|(0.04)|
|**Years of schooling X short**|||**0.06****|||**0.05**|
||||**(0.03)**|||**(0.05)**|
|**Years of schooling X proxy**|||**0.04**|||**0.00**|
||||**(0.03)**|||**(0.06)**|
|Short||0.06|‐0.35||‐0.06|‐0.29|
|||(0.11)|(0.22)||(0.22)|(0.33)|
|Proxy||0.22|‐0.09||0.25|0.23|
|||(0.14)|(0.28)||(0.19)|(0.37)|
|� : District dummies|yes|yes|yes|yes|yes|yes|
|Observations|192|192|192|99|99|99|
|R‐squared|0.44|0.45|0.47|0.37|0.39|0.39|

Standard errors, clustered at the household level, in parentheses; *** p<0.01, ** p<0.05, * p<0.1. All regressions include control variables: age, age squared, and a constant 31
Table 2: PMG Test Results – Group Score

|**Variable:**|**Coefficient**|**Standard Error**|
|---|---|---|
|**_Long run:_**|||
|_GDPPC_|_-2.2986_|_1.972_|
|_FX_|_129.4836_|_1483.247_|
|_MMIR_|_-77039.64**_|_30387.31_|
|_HICP_|_-418.4206*_|_113.3164_|
|_IPI_|_12.235_|_41.4485_|
|_BOPNFA_|_3.0562*_|_1.0272_|
|_ECT****_|_-0.088 [0.075]***_|_0.0494_|
|**_Short run:_**|||
|_GDPPC_|_-0.4891_|_0.5794_|
|_FX_|_36805.34_|_23138.24_|
|_MMIR_|_3245.045_|_2556.772_|
|_HICP_|_27.4077_|_31.0176_|
|_IPI_|_3.2396_|_3.1278_|
|_BOPNFA_|_0.0083_|_0.1052_|
|_Constant_|_4653.075***_|_2549.367_|
|_Log likelihood_|_-1480.709_||
|_Hausman test_|_3.13 [0.4213]_||

The Hausman test indicates PMG consistency with _p_ -value > 5 percent, thus illustrating non-significance to reject a null hypothesis of systemic difference in coefficients. From thereon, PMG long-run coefficients fixing prevails as the choice of method. The log-likelihood value determines the plausibility function of the model under the observed data.

_Note: The optimal selection order criteria is determined by Schwarz Bayesian information criterion (“SBIC”). ARDL (4,4) and data interval 2006Q1-2016Q1 is chosen. [] is p-value._ _* Null hypothesis is rejected at 1 percent level._ _** Null hypothesis is rejected at 5 percent level._ _*** Null hypothesis is rejected at 10 percent level._ _**** Error correction term._ In the results of the sample data panel PMG test for the aggregate Southeastern European group the relationship between macroeconomic indicators and stock prices is statistically 16 | P a g e
28

# Table 4: Estimates of impact of UC on employment

|||||Ye|ars since im|plementatio|n||
|---|---|---|---|---|---|---|---|---|
||||-3|-2|0|1|2|3|
|Probability of|inl ml|coef|-0.053*|-0.026|0.024**|0.038**|0.050*|0.070*|
|working|sge ae|t|-1.72|-1.46|2.35|1.98|1.84|1.91|
||id l|coef|-0.010|-0.007|0.003|0.008|0.015|0.016|
||marre mae|t|-1.09|-1.12|0.72|1.10|1.39|1.05|
||il fl|coef|-0.012|-0.020*|0.011|0.023*|0.036**|0.075***|
||snge emae|t|-0.76|-1.91|1.37|1.74|2.02|2.71|
||id fl|<sup>coef</sup>|-0.060*|-0.025|0.025**|0.061**|0.080**|0.116**|
||marre emae|<br>t|-1.68|-1.09|2.15|2.41|2.30|2.37|
|Formal<br>|<sup>sinle male</sup>|coef|-0.021*|-0.011|-0.009|-0.018|-0.026|-0.030*|
|employment|<sup>g</sup>|t|-1.82|-1.18|-1.10|-1.26|-1.59|-1.75|
||id l|coef|0.006|0.004|-0.002|-0.015|-0.033*|-0.032|
||marre mae|t|0.37|0.35|-0.25|-1.14|-1.71|-1.64|
||il fl|coef|-0.009|-0.011**|0.002|0.001|-0.005|-0.001|
||snge emae|t|-1.40|-2.11|0.54|0.26|-0.55|-0.12|
||id fl|<sup>coef</sup>|-0.015|-0.001|0.003|-0.003|-0.009|-0.004|
||marre emae|<br>t|-1.44|-0.15|0.43|-0.28|-0.70|-0.23|
|Informal<br>|<sup>il l</sup>|coef|-0.030|-0.013|0.034**|0.058**|0.078**|0.102**|
|employment|<sup>snge mae</sup>|t|-0.86|-0.67|2.41|2.08|2.16|2.14|
||id l|coef|0.006|0.008|0.014|0.040*|0.069**|0.074*|
||marre mae|t|0.17|0.34|1.20|1.74|2.10|1.80|
|||coef|0.004|-0.003|0.011|0.026|0.046**|0.082***|
||single female|t|0.20|-0.27|1.19|1.62|2.02|2.75|
|||<sup>coef</sup>|-0.043|-0.022|0.024|0.067**|0.093**|0.125**|
||married female|<br>t|-1.05|-0.88|1.56|2.08|2.09|2.06|

Notes: *** p<0.01, ** p<0.05, * p<0.1. Results are from estimates of eqn (1) with the coefficients on UC and VF constrained as described in section IV. The regressions include – in addition to the UC dummies – the VF dummies, province- and quarter-specific fixed effects, as well as as the provincial minimum wage, educational attainment, age, the demographic mix of the respondent’s household, area of residence (rural versus urban). Standard errors are adjusted for clustering at the province level.
Table A3 Regional Inequality in the Distribution of Welfare, 2001

||Gini|Atkinson-1|Atkinson-2|Mean LogDeviation|Theil|
|---|---|---|---|---|---|
|Douala|42.46|26.16|39.33|30.33|41.17|
|Yaoundé|42.59|26.00|39.88|30.11|37.79|
|Adamaoua|33.82|16.87|28.81|18.48|20.14|
|Center|34.62|18.55|34.61|20.52|22.06|
|East|34.21|17.66|31.33|19.43|20.26|
|Far North|32.97|16.05|27.77|17.49|18.69|
|Coast|34.19|17.62|31.36|19.39|20.24|
|North|36.16|19.23|31.11|21.36|25.62|
|North West|40.55|24.40|41.68|27.98|29.96|
|West|31.21|14.69|25.49|15.89|17.61|
|South|29.76|13.27|23.19|14.24|15.45|
|South West|38.02|21.41|35.88|24.09|26.81|
|Cameroon|40.41|24.00|38.82|27.45|33.75|

Source: Authors’ Calculations TableA4 Regional Inequality in the Distribution of Welfare, 2007

||Gini|Atkinson-1|Atkinson-2|Mean LogDeviation|Theil|
|---|---|---|---|---|---|
|Douala|33.87|17.07|28.37|18.72|21.72|
|Yaoundé|33.15|16.60|28.02|18.15|21.07|
|Adamaoua|33.75|16.70|27.25|18.27|21.20|
|Center|28.07|11.91|20.72|12.68|14.13|
|East|32.88|15.79|26.63|17.19|18.99|
|Far North|36.52|19.14|30.28|21.24|25.07|
|Coast|31.86|15.33|25.71|16.64|19.26|
|North|35.33|18.22|28.57|20.12|24.65|
|North West|38.24|20.98|33.32|23.54|27.66|
|West|29.73|13.39|23.41|14.37|15.80|
|South|34.58|18.02|29.79|19.87|23.61|
|South West|33.24|16.54|28.67|18.08|19.69|
|Cameroon|38.96|21.94|35.82|24.77|27.88|

Source: Authors’ Calculations Table A5 Shapley Decomposition of Urban- Rural Differences in 1996

|||Difference|Scale|Inequality|
|---|---|---|---|---|
||Headcount|-11.87|-16.12|4.26|
||Poverty Gap|-4.43|-8.51|4.09|
|Urban|Squared Poverty Gap|-2.09|-4.96|2.87|
||Watts|-6.12|-13.24|7.12|
||Headcount|6.36|13.12|-6.76|
||Poverty Gap|2.37|6.91|-4.53|
|Rural|Squared Poverty Gap|1.12|4.10|-2.98|
||Watts|3.28|10.80|-7.52|

Source: Authors’ Calculations 45
**Table S4.E continued**

||(6)|(7)<br>|(8)<br>|(9)<br>|
|---|---|---|---|---|
|VARIABLES|suprat|htrain|ftrain|pcf_hk|
|supbuy|-0.0227|0.0811|-0.0429|-0.0489|
||(0.0340)|(0.107)|(0.133)|(0.221)|
|samesup|0.0134|0.0440|0.0120|0.0864|
||(0.0141)|(0.0473)|(0.0414)|(0.0801)|
|samecus|0.0165|0.0394|0.126**|0.214*|
||(0.0237)|(0.0743)|(0.0631)|(0.124)|
|same_sec|-0.00250|-0.0173|-0.0303*|-0.0479*|
||(0.00490)|(0.0123)|(0.0177)|(0.0263)|
|ldist|-0.00638|-0.0147|-0.00460|-0.0129|
||(0.00486)|(0.0103)|(0.0116)|(0.0215)|
|ad_fmage|-0.000149|-0.000207|0.00180|0.00225|
||(0.000335)|(0.000721)|(0.00114)|(0.00210)|
|ad_meduc|-0.00409*|0.0113|0.00684|0.0332|
||(0.00240)|(0.0184)|(0.0161)|(0.0352)|
|ad_mexper|0.000210|-0.00167**|-0.000960|-0.000827|
||(0.000476)|(0.000804)|(0.000843)|(0.00164)|
|ad_mfemale|0.00760|0.0182|-0.0118|-0.00519|
||(0.0112)|(0.0258)|(0.0177)|(0.0401)|
|ad_lemp|0.0108**|0.0877***|0.0991***|0.230***|
||(0.00494)|(0.0155)|(0.0142)|(0.0308)|
|sametown|-0.0275|0.0131|0.0664|0.0752|
||(0.0304)|(0.0610)|(0.0659)|(0.130)|
|Constant|0.198***|0.310***|0.211***|0.627***|
||(0.0323)|(0.0703)|(0.0708)|(0.133)|

Standard errors in parentheses *** p<0.01, ** p<0.05, * p<0.1 14
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HICs while the proportion of firms that have a bank loan or line of credit is significantly higher (at 10 percent level) in the UMICs. The percentage of firms’ senior management’s time that is spent on dealing with government regulations ( _Time Tax_ ) is used as a broad proxy measure of the regulatory burden on the firms and the business environment in which the firms operate. _Time Tax_ for a typical firm in UMICs equals 12.7 percent compared to a slightly lower value of 11.5 percent in the HICs, and the difference is significant at 5% level. This indicates that the regulatory burden on firms operating in UMICs is higher.

We also include some country-level variables in our regressions. These are intended to account for differences across countries in the level of human capital, market size, quality of governance, and institutional strength. For human capital, we use three different measures, namely: gross primary enrollment rate, gross secondary enrollment rate, and gross tertiary enrollment rate taken from World Development Indicators, World Bank. For market size, we use the log of the total population in the country. For the quality of governance, we use the Transparency International’s Corruption Perception Index ( _Corruption_ ), and to capture the institutional strength and protection of private property against expropriation by state and private agents we use the Law and Order index from ICRG. Note that higher values of all the macro-level variables described above imply a more favorable business environment from the firms’ point of view. As depicted in Table 2, market size is lower in the HICs than the UMICs (by about 59 percent) and the difference insignificant at the 1 percent level. The enrollment rate in primary education is also higher in the UMICs than in HICs by about 5.7 percentage points, and the difference is significant at the 1 percent level. However, all the remaining macro-level indicators including corruption, law and order, enrollment rates in secondary and tertiary education, HICs outperform UMICs, with the difference significant at the 1 percent level.

10