rafmacalaba/datause-encoder-data huggingface dataset:
48 approximately constant across the quintile groups. All food items were deflated by an index provided in the survey data set.

The survey asked whether households used LPG and how much they paid per month on average. The survey asked what LPG and electricity were used for but did not have a question on the main cooking fuel. Monthly average retail prices of LPG from four main suppliers were available and the expenditure on LPG recorded in the previous month (May through July, 2008) was divided by the national average price for that month to estimate the quantity purchased by each household.

At least two thirds of households in each quintile group used LPG except the bottom rural quintile. The share of households using LPG peaked in the middle quintiles and then declined. Although not shown in the table, among households not using LPG for cooking, the proportion using electricity for cooking increased with quintile level: for the bottom rural quintile only 31 percent used electricity instead, while for the top urban quintile 89 percent used electricity for cooking. The quantity purchased per capita steadily increased with quintile level, and the quantity per household also generally increased, except for the top quintile in rural areas (Table A2.17).

Table A2.17: Number of households purchasing LPG and estimated average quantity purchased per month by purchasing households in Albania

|**Quintile**|**Number of p**<br>**Rural**|**urchasing househo**<br>**Urban**|**lds (percent)**<br>**Total**|**Kg purchase**<br>**Rural**|**d per household**<br>**Urban**|**(per capita)**<br>**Total**|
|---|---|---|---|---|---|---|
|**1**|38,983 (56)|30,621 (71)|69,604 (62)|8.4 (1.5)|10.5 (2.3)|9.3 (1.9)|
|**2**|48,167 (67)|37,851 (68)|86,018 (68)|9.7 (2.0)|10.1 (2.6)|9.9 (2.2)|
|**3**|53,483 (74)|57,190 (80)|110,673 (77)|10.1 (2.5)|12.0 (3.3)|11.1 (2.9)|
|**4**|58,524 (80)|68,869 (75)|127,393 (77)|13.3 (3.5)|12.7 (4.0)|12.9 (3.7)|
|**5**|50,371 (79)|91,561 (67)|141,932 (71)|11.9 (4.2)|13.0 (5.1)|12.6 (4.8)|
|**All**|249,528 (71)|286,092 (72)|535,620 (71)|10.9 (2.8)|12.1 (3.8)|11.5 (3.4)|

_Source:_ Authors’ calculations. _Note_ : See notes for Table A2.3.

The expenditure share of LPG averaged across all households declined steadily in urban areas and also declined from the fourth to the top quintile in rural areas. The expenditure share was higher in urban areas in all cases except the top quintile. For user households, the expenditure share generally declined with quintile level (Table A2.18).
# **2 Green Goods Trade Landscape and Labor Markets**

## **2.1 Data**

We rely on data on NTMs, tariffs, and trade values from UNCTAD and WITS from 2014 to 2020 to calculate the AVEs. The data is disaggregated at the 6-digit level of the HS product classification. The NTM data is from the UNCTAD TRAINS (United Nations Conference on Trade and Development - Trade Analysis Information System) dataset, while the tariff and trade data are from the World Integrated Trade Solution (WITS) dataset. The NTM data uses a cut-off query every December 31st to get the annual period from 2008 to 2020, although the data was collected in 2015 and then again in 2018 in collaboration with the Economic Research Institute for ASEAN and East Asia (ERIA). The limit to this data is that this is not a true panel but a “manufactured” panel.

The second database used is the Philippines Labor Force Survey (LFS), which provides insights into the employment status of individuals in the labor force. It includes data on annual earnings, workers’ industrial affiliation, occupation, education level, and other related labor market aspects. The LFS data is collected quarterly, and for this analysis, we focused on the surveys conducted between 2010 and 2020 at the month of October to ensure consistency. It is important to note that the LFS invites different individuals in each round, making it a repeated cross-sectional dataset rather than a proper panel dataset. Although – the data is based on samples it includes weights that allow to make generalizations about the total labor force.

These two databases were merged with additional datasets that identify products classified as green goods. The green goods referred to in this note are based on the list of green products defined by the Green Transition Navigator (GTN)–a compilation of the APEC, OECD, and WTO green goods classifications. To arrive at our final database, we followed the following steps: first, we cleaned the trade data and ensured consistent HS product classification was used across the years. Next, we merged this data with the GTN list of green 4
# **_Table A4.7c Relationship between staff experience, staff predicted performance, and IEG outcome rating, weighted by original commitment (in millions, constant 2015 $)_**

||**De**<br>**Staffp **<br>|**pendent variab**<br>**redictedperfor**<br>|**le:**<br>**mance**<br>|**De**<br>**IEG**<br>|**pendent varia**<br>**1-6 outcome r**<br>|**ble:**<br>**ating**<br>|
|---|---|---|---|---|---|---|
||**(1)**|**(2)**|**(3)**|**(4)**|**(5)**|**(6)**|
|Number of projects prepared|0.026**|||-0.007|||
|before by the preparation TTL|(0.013)|||(0.010)|||
|(Log) Number of ISRs signed<br>before by the mid-point<br>supervision TTL||0.072***<br>(0.020)|||0.021<br>(0.030)||
|(Log) Number of ISRs signed|||0.053***|||0.083**|
|before by the final supervision<br>TTL<br>Constant|0.481***|0.300***|(0.019)<br>0.258***|2.337***|2.286***|(0.038)<br>2.105***|
||(0.039)|(0.061)|(0.060)|(0.462)|(0.476)|(0.444)|
|Observations|2,038|2,560|2,513|2,767|2,762|2,757|
|Adjusted R-squared|0.001|0.004|0.003|0.208|0.208|0.214|

Note: Robust standard errors clustered at the country level are reported in parentheses. (***p<0.01, **p<0.05, *p<0.10). All regressions are estimated using OLS, weighted by original commitment amount. Columns (1) (2) (3) do not include any controls. Columns (4) (5) (6) include controls for region, sector, IBRD/IDA, original commitment amount of project, original length of project, CPIA score of project country at approval, change in the CPIA score over project lifetime and IEG evaluator fixed effects.

# **Table A4.7d Relationship between staff experience, staff predicted performance, and IEG outcome rating, mean imputation for missingness**

||**De**<br>**Staffpr **<br>|**pendent varia**<br>**edictedperfo**<br>|**ble:**<br>**rmance**<br>|**De**<br>**IEG**<br>|**pendent varia**<br>**1-6 outcome r**<br>|**ble:**<br>**ating**<br>|
|---|---|---|---|---|---|---|
||**(1)**|**(2)**|**(3)**|**(4)**|**(5)**|**(6)**|
|Number of projects prepared|0.068***|||-0.013|||
|before by the preparation TTL|(0.014)|||(0.010)|||
|(Log) Number of ISRs signed before||0.062***|||0.011||
|by the mid-point supervision TTL||(0.020)|||(0.021)||
|(Log) Number of ISRs signed before|||0.058***|||0.041*|
|by the final supervision TTL|||(0.019)|||(0.023)|
|Constant|-0.105***|-0.184***|-0.230***|2.051***|2.035***|2.007***|
||(0.039)|(0.057)|(0.060)|(0.344)|(0.345)|(0.320)|
|Observations|2,038|2,560|2,513|2,767|2,767|2,767|
|Adjusted R-squared|0.011|0.004|0.003|0.105|0.105|0.106|

Note: Robust standard errors clustered at the country level are reported in parentheses. (***p<0.01, **p<0.05, *p<0.10). All regressions are estimated using OLS, weighted by original commitment amount. Columns (1) (2) (3) do not include any controls. Columns (4) (5) (6) include controls for region, sector, IBRD/IDA, original commitment amount of project, original length of project, CPIA score of project country at approval, change in the CPIA score over project lifetime and IEG evaluator fixed effects. Mean imputation used for missing values of TTL experience.

58
As mentioned earlier, the statistics on jobs that would have been created and jobs that would have been destroyed in absence of rigid labor regulations ignore within-firm worker turnover. We therefore define the additional hires that would have occurred as a percentage of total employment as and the additional fires that would have occurred as a percentage of total employment as Recall that the net percentage change in employment could be decomposed into its job creation and job destruction components. Similarly, we can decompose the net percentage change in employment into its hiring and firing components as follows It may also be useful to note that I will present some of the results separately by industry and by firm size. These calculations are straightforward. For example, if one would like to calculate the above statistics only for the manufacturing sector, one simply needs to exclude all non-manufacturing firms from the data set and proceed as described above.

I will also make use of an indicator of labor market rigidity. I use the index from the The Economic Freedom of the World (EFW) report published by the Fraser Institute. The index is a function of six indicators: a measure of the importance of minimum wages, the legally mandated cost of hiring, the legally mandated cost of worker dismissal, firm perceptions of the importance of hiring and firing regulations, firm perceptions on the importance of collective bargaining, and an index of the importance of military conscription. I standardize the index, setting the mean to be zero and the standard deviation to be one for the 14 countries used in the analysis. I then multiply by minus one so that high values of the index now correspond to more rigid labor markets.

9
from 17% to 25%. For the Tanzania data, about one-third (36%) of the poor put vignette 1 above rung 1, giving _SHP_ =0.926.

The choice of vignette 1 as the “poverty line” for these calculations is natural, but it is of interest also to consider the implications of using vignette 2 instead. Using the raw (uncorrected) data the poverty rates are then 34%, 74%, and 44% respectively, while the corresponding vignette-consistent rates are 61%, 84%, and 79%.

While cross-country comparability is questionable (as already noted), it is at least notable that the ranking of the three countries in terms of poverty is identical, with and without the vignette correction for scale heterogeneity. The Guatemala sample is the poorest, followed by Tanzania, and Tajikistan the least poor. This holds for both vignettes 1 and 2 as the reference.

The two measures of subjective poverty also share similar covariates. Table 3 gives probits for the two measures of subjective poverty—the measure in which one deems the respondent to be poor if she puts herself on the lowest rung and our proposed new measure. Despite the substantial scale heterogeneity we find among the subjectively poor, there is a striking similarity in the two probits. There are only a few cases in which a coefficient is significant in one and not the other, and the sizes of the coefficients are generally similar.

# **4. Tests for bias in subjective welfare regressions**

The subjective poverty measures discussed above naturally ignore rankings among the non-poor, in keeping with the usual focus axiom in poverty measurement. We turn next to regression analysis of the full range of welfare rankings.

First we ask whether vignette responses are correlated with covariates commonly found in subjective welfare regressions in the literature, including objective measures of economic welfare. We assume an ordered probit specification, which has become standard in the literature. However, it should be noted that this specification requires a further restriction on the nature of the heterogeneity problem, as described in the model represented by equations (2) and (3). Specifically, we also need to assume that the heterogeneity takes the form of homogeneous shifts in the scales, such that τ _ik_ −τ _ik_ − 1 is constant across all _i_ for each _k_ . Thus we can define a new, scale-transformed, continuous variable, _SWi j_ * , by adding an appropriate (individual- and 13
27 Table 7: Sensitivity of results to sample used in probit estimation

|||Probit on f<br>|ull sample<br>|||Probit on eligi<br>|ble subsample<br>||
|---|---|---|---|---|---|---|---|---|
||Differences v<br>propensity sc<br>|ia matching:<br>ore weights<br>|Differences v<br>kernelw<br>|ia matching:<br>eights<br>|Differences v<br>propensity sc<br>|ia matching:<br>ore weights<br>|Differences v<br>kernel<br>|ia matching:<br>weights<br>|
||coeff.|t-stat|coeff.|t-stat|coeff.|t-stat|coeff.|t-stat|
|Total out-of-pocket payments|18.340|2.04|-6.249|-0.62|14.022|1.66|2.507|0.29|
|Catastrophic (>10% nonfood consumption)|-0.033|-2.28|-0.027|-2.25|-0.042|-3.22|-0.046|-4.54|
|Outpatient expenses|12.837|1.94|5.805|0.80|10.085|1.68|6.644|1.15|
|Inpatient expenses|26.522|2.01|18.115|1.25|27.640|2.20|24.040|1.86|
|Outpatient expenses per visit|5.511|0.62|-1.211|-0.14|-0.905|-0.10|-0.792|-0.09|
|Inpatient expenses per admission|169.231|1.49|18.861|0.15|122.522|1.08|67.531|0.54|
|Non-prescribed medicine expenses|2.446|1.32|-3.476|-1.47|-0.284|-0.16|-3.578|-2.04|
|Medical equipment expenses|0.124|0.90|-0.059|-0.48|-0.118|-1.45|-0.218|-2.42|
|# outpatient visits|0.224|4.18|0.168|3.20|0.197|3.83|0.144|2.73|
|# inpatient spells|0.036|2.27|0.047|3.49|0.042|2.94|0.044|3.35|
|Outpatient visits Yes|0.046|3.46|0.045|4.25|0.045|3.76|0.039|3.74|
|Inpatient spells Yes|0.011|1.22|0.021|3.42|0.018|2.51|0.020|3.13|
|Village station Yes|-0.003|-0.54|0.004|1.69|-0.001|-0.19|0.002|0.97|
|Commune center Yes|0.033|2.77|0.048|5.55|0.046|4.65|0.047|5.67|
|Regional clinic Yes|0.000|-0.01|0.001|0.21|0.001|0.25|0.001|0.27|
|District hospital Yes|0.026|3.77|0.022|3.83|0.025|3.97|0.022|3.77|
|Provincial hospital Yes|0.021|5.02|0.019|4.78|0.020|5.13|0.019|5.65|
|Central hospital Yes|0.001|0.59|0.000|-0.24|0.001|0.61|0.000|0.33|
|Other govt hospital Yes|0.001|0.68|0.000|0.00|0.001|0.55|0.000|0.26|
|Private provider Yes|-0.010|-1.65|-0.021|-3.64|-0.022|-3.48|-0.029|-4.82|
|Traditional healer Yes|0.000|0.21|0.001|0.31|0.001|0.41|0.001|0.27|

Note: Expenditures are in thousands of VND, except in case of catastrophic out-of-pocket payments which is a share of the relevant subsample.
11 international investors may reorient portfolios away from Mexico as opportunities to invest in high-yield securities open up in other parts of the world.

# D. Micro and Macro Factors Affecting the Costs of Capital In Mexico

26. Although substantial reforms were implemented to the financial svstem involving extensive deregulation of financial services over the 1988-92 period, nominal and ex-post real lending rates of interest before and after taxes have remained high. Chart I plots both the lending rates by commercial banks and the government cost of funds as reflected in CETES rates. It also shows the average cost of funds to commercial banks as measured by CPP. The spreads between CPP and Cetes has been widening over the 1989-93 period.<sup>5</sup> This is due to the fact that the investment grade rating obtained for CETES elicited a strong demand by foreigners. The growing spread also reflects the fact that the relative solvency of the banking system has deteriorated slightly since the time of privatization.!

27. Underlying these high interest rates are both macroeconomic and microeconomic factors. Looking at these helps to highlight the types of <!-- Start of picture text --> Chart I<br>Costo del Credito y Margen de<br>Intermediacion<br>Tasas Noinales en Por Cientos<br>60<br>Je40<br>0 I,., *,, *l**co T_<br>oo;0) OD OD 0D m OD ODm<br>Tasa Activa Tasa Activa CPP CPP<br><!-- End of picture text --> channels through which NAFTA provisions could have a beneficial effect on efficiency of financial intermediation in Mexico. Although the numbers are not precise, and are based on average. rather than marginal funding rates to commercial banks, the decomposition in Table 5 gives a sense for the magnitudes involved.' This widening would also occur if the new average interbank rate were used (TIIP).

By June 1993, non-performing loans (as a percentage of total loans) had deteriorated to about 7.2 percent versus 5 percent in 1991-92.

7The approximation error introduced by using CPP rates (i.e a weighted average of all rates quoted on different commercial bank liabilities) is not that large to the extent that all liabilities, even checking accounts, pay interest.
<!-- Start of picture text -->
-40-<br><!-- End of picture text -->

# TABLE A-2 PUBLIC SECTOR EMPLOYMENT

(in percent of labor force) <!-- Start of picture text --> PUBLIC SECTOR EMPLOYMENT _<br>General Civilian Government Employment<br>OECD Government Administration Social Sectors Total<br>Central Non Central Education Health General Civilian Armed<br>Government Govemment Government Forces<br>Australia 2.8 4.9 4.8 3.6 16.2 0.7<br>Austria 7.7 5.7 1.2 2.9 17.5<br>Belgium 4.2 5.6 6.7 1.9 18.4 1.1<br>Canada 2.1 3.7 2.6 8.4 0.5<br>Denmark 5.0 9.3 4.9 3.6 22.8 1.2<br>Finland 4.3 15.1 1.4 3.5 24.3 1.2<br>France 6.1 4.8 4.2 0.5 15.6 1.6<br>Germany 0.7 5.0 2.6 0.3 8.6 0.8<br>Greece 2.9 1.0 3.1 0.2 7.2 4.4<br>Ireland 2.6 1.7 3.3 3.8 11.5 0.9<br>Italy 3.3 3.1 4.1 2.6 13.2 1.4<br>Japan 1.4 2.4 2.0 0.4 6.3 0.4<br>Netherlands 8.5 2.8 11.3<br>New Zealand 2.6 2.5 13.4 18.5<br>Norway 5.2 3.2 6.1 11.1 25.7<br>Portugal 3.5 1.7 3.9 0.9 10.0 1.1<br>Spain 3.4 5.2 1.5 10.2 1.4<br>Sweden 8.1 10.6 6.6 9.4 34.7 1.5<br>Switzerland 4.6 5.5 3.8 2.9 16.9 0.1<br>United Kingdom 2.7 4.7, 3.6 3.6 14.5 1.1<br>US 2.5 6.61 4.71 1.0 14.7 0.2<br>.AVERAGE 3.9 5.31 3.7 2.8 15.6 1.1<br><!-- End of picture text --> <!-- Start of picture text --> (in percent of total employment)<br>I_________________ gPUBLIC SECTOR EMPLOYMENT |<br>General Civilan Government Employment ..<br>OECD Goverment Administration Social Sectors Total<br>Central Non Central Education Health General Civilian Armed<br>Government Government Govemment Forces<br>Australia 3.1 5.4 5.3 4.0 17.7 0.7<br>Austria 8.1 6.0 1.2 3.0 18.3<br>Belgium 4.7 6.2 7.6 2.1 20.6 1.3<br>Canada 2.3 4.1 2.9 9.3 0.6<br>Denmark 5.5 10.3 5.5 3.9 25.2 1.3<br>Finland 5.5 19.4 1.8 4.5 31.2 1.6<br>France 6.91 5.5 4.8 0.6 17.8 1.8<br>Germany 0.8 5.5 2.9 0.3 9.5 0.9<br>Greece 3.2 1.1 3.5 0.2 8.0 4.9<br>Ireland 3.1 2.0 4.0 4.5 13.6 1.1<br>Italy 3.7 3.6 4.7 3.0 15.0 1.6<br>Japan 1.5 2.5 2.1 0.5 6.5 0.4<br>Netherlands 9.2 3.0 12.2<br>New Zealand 2.9 2.7 14.5 20.0<br>Norway 5.5 2.7 6.5 11.8 26.5<br>Portugal 3.7 3.4 4.1 0.9 12.1 1.2<br>Spain 4.5 1.7 2.0 8.3 1.8<br>Sweden 8.9 6.9 7.2 10.3 33.3 1.6<br>Switzerland 4.9 11.6 4.0 3.0 23.5 0.1<br>United Kingdom 3.0 5.8 3.9 4.0 16.7 1.2<br>US 2.6 5.2 4.9 1.1 13.8 0.2<br>AVERAGE 4.3 5.7 4.1 3.0 17.2 1.2<br><!-- End of picture text -->

Column averages encompass only those countries for which the information is complete
- Allingham, M. G. and A. Sandmo (1972). Income tax evasion: a theoretical analysis. _Journal of Public Economics 1_ (3-4), 323–338.

- Antinyan, A. and Z. Asatryan (2020). Nudging for tax compliance: A meta-analysis. _cesifo Working Papers 8500_ .

- Ariel, B. (2012). Deterrence and moral persuasion effects on corporate tax compliance: findings from a randomized controlled trial. _Criminology 50_ (1), 27–69.

- Besley, T. and T. Persson (2013). Taxation and development. In _Handbook of public economics_ , Volume 5, pp. 51–110. Elsevier.

- Blumenthal, M., C. Christian, J. Slemrod, and M. G. Smith (2001). Do normative appeals affect tax compliance? Evidence from a controlled experiment in Minnesota. _National Tax Journal_ , 125–138.

- Brockmeyer, A., A. M. Estefan, K. R. Arras, and J. C. S. Serrato (2020). Taxing property in developing countries: Theory and evidence from mexico. _Working Paper_ .

- Brockmeyer, A., S. Smith, M. Hernandez, and S. Kettle (2019). Casting a wider tax net: Experimental evidence from Costa Rica. _American Economic Journal: Economic Policy 11_ (3), 55–87.

- Callaway, B. and P. H. Sant’Anna (2021). Difference-in-differences with multiple time periods. _Journal of Econometrics 225_ (2), 200–230.

- Carril, A. et al. (2017). Dealing with misfits in random treatment assignment. _Stata Journal 17_ (3), 652–667.

- Castro, L. and C. Scartascini (2015). Tax compliance and enforcement in the Pampas evidence from a field experiment. _Journal of Economic Behavior & Organization 116_ , 65–82.

- Cohen, I. (2020). Low-cost tax capacity: A randomized evaluation with the Uganda Revenue Authority.

- Coleman, S. (1996). The Minnesota income tax compliance experiment.

- Del Carmen, G., E. E. Espinal Hernandez, and T. Scot (2020). Targeting in tax compliance interventions: Experimental evidence from honduras. _Working Paper_ .

- DellaVigna, S. and E. Linos (2020). Rcts to scale: Comprehensive evidence from two nudge units. Technical report, National Bureau of Economic Research.

- Dincecco, M. and G. Katz (2014). State capacity and long-run economic performance. _The Economic Journal 126_ (590), 189–218.

- Fellner, G., R. Sausgruber, and C. Traxler (2013). Testing enforcement strategies in the field: Threat, moral appeal and social information. _Journal of the European Economic Association 11_ (3), 634–660.

- Fjeldstad, O.-H., M. Ali, and L. Katera (2017). Taxing the urban boom in Tanzania: Central versus local government property tax collection. _CMI Insight_ .

- Gadenne, L. (2017). Tax me, but spend wisely? sources of public finance and government accountability. _American Economic Journal: Applied Economics_ .

17
||**Language**|**Math**|**Exec.**<br>**function**|**Socio-emo.**|**Overall**<br>**readiness**|
|---|---|---|---|---|---|
||(0.054)|(0.059)|(0.071)|(0.054)|(0.057)|
|**Child age (in months)**|0.058***|0.056***|0.040***|0.017**|0.059***|
||(0.004)|(0.004)|(0.005)|(0.005)|(0.004)|
|**Constant**|-3.847***|-3.833***|-2.553***|-0.915*|-3.898***|
||(0.352)|(0.394)|(0.384)|(0.390)|(0.361)|
|**N (obs.)**|1308|1308|1308|1308|1308|
|**R-sq.**|0.327|0.318|0.194|0.127|0.359|
|**P-val. model**|0.000|0.000|0.000|0.000|0.000|
|**P-val. Moth. ed.**|0.153|0.016|0.030|0.597|0.023|
|**P-val. Fath. ed**|0.118|0.160|0.210|0.087|0.296|
|**P-val. Moth. oc.**|0.442|0.757|0.611|0.569|0.418|
|**P-val. Fath. oc.**|0.781|0.347|0.769|0.448|0.670|

# _Source:_ Authors’ calculations

_Notes:_ *p<0.05; **p<0.01; ***p<0.001. Standard errors clustered on the school level.

# **_4.3 Inequality in inputs_**

We now turn to examining inequality in home and pre-primary inputs by SES. In Figure 5, we explore the patterns of pre-primary quality and home inputs by the families’ asset index, based on a local polynomial (triangle kernel). Figure 6 shows the variation in stimulation by mother’s and father’s education and father’s occupation. Table 2 shows OLS models for SES and the various home and pre-primary inputs (testing H2 and H3). There is substantial variation in the relationship between inputs and assets. Strong relationships were observed between home stimulation and preprimary environments, and the family asset index. A one SD increase in the asset index predicts a statistically significant 0.197 SD increase in home stimulation (consistent with H2). There are similar (and likewise significant) relationships of around 0.19 SD increases in the pre-primary environment or teacher attitudes for each SD increase in assets (consistent with H3). Other results for teaching practices (coefficient of 0.117), materials (-0.006) and adherence to the curriculum (- 0.069) were not significantly associated with family assets.

17