rafmacalaba/datause-encoder-data huggingface dataset:
**Table 8: Balancing of Covariates after Propensity Score Matching, Georgia**

||R|aw<br>|Kernel M|atching<br>|5:1 NN|Matching<br>|
|---|---|---|---|---|---|---|
|||No-||No-||No-|
||Support|Support|Support|Support|Support|<br>Support|
|Firm Size|||||||
|Small (0-19)|0.669|0.642|0.685|0.658|0.679|0.636|
|Medium (20-99)|0.249|0.333|0.262|0.303|0.263|0.335|
|Large (100+)|0.082|0.025**|0.053|0.039|0.058|0.029|
|Firm Age|||||||
|0-10 years|0.572|0.580|0.562|0.546|0.558|0.520|
|11-20 years|0.319|0.329|0.322|0.344|0.327|0.351|
|20+ years|0.109|0.091|0.117|0.111|0.115|0.130|
|Manufacturing|0.207|0.152|0.173|0.165|0.170|0.174|
|Construction|0.172|0.170|0.186|0.148|0.183|0.104*|
|Retail|0.045|0.037|0.048|0.037|0.048|0.051|
|Year of the top manager's<br>experience|0.292|0.225|0.301|0.297|0.296|0.313|
|Exporting directly 10% of sales|15.972|15.589|15.876|15.799|15.747|16.422|
|At least 10% of foreign owned|0.128|0.080|0.125|0.113|0.125|0.108|
|Product/service or process|||||||
|innovation|0.059|0.052|0.046|0.061|0.054|0.070|
|Part of a larger firm|0.537|0.426|0.512|0.428|0.506|0.434|
|Log sales per worker|0.049|0.032|0.048|0.049|0.047|0.056|
|Majority female owned|9.939|9.591|9.939|9.744|9.932|9.691|
|Fully credit constrained|0.095|0.180|0.095|0.132|0.102|0.118|
|Bank loan/line of credit|0.009|0.168***|0.008|0.037|0.010|0.040|
|Experiencing electrical outages|0.536|0.406|0.546|0.481|0.545|0.515|
|Offering formal training|0.460|0.501|0.464|0.467|0.469|0.497|
|N|2|35|21|9||217|

Note: This table shows the balancing of firm characteristics in Georgia when propensity score is used as matching metric. Raw columns describe firms before matching happens. Kernel matching columns are for the firms that are matched according to the kernel matching algorithm. 5:1 NN Matching columns are for the firms that are matched according to the 5 to 1 nearest neighbor matching algorithm. Support columns show the respective statistics of firms that have received government support. No-Support columns show the respective statistics of firms that have not received any government support. N denotes the number of observations. Asterisks in the No-Support columns are for unconditional t-tests comparing the respective statistic with the same statistic for the firms that received support. All standard errors are clustered at survey strata, and *, **, *** denotes the significance level at 10%, 5%, and 1%, respectively. See Section 5 for more details.

41
156 86 Obs.

|**_Panel D. Ecuador_**|||
|---|---|---|
|HEI is public|0.346***|0.372***|
|Obs.|543|483|
|Total graduates|20.2**|19.9**|
|Obs.|465|405|
|Annual tuition cost|-387.2|-107.4|
|Obs.|333|291|
|Program is taught online|0.058*|0.052|
|Obs.|543|484|
|**_Panel E. Peru_**|||
|Technical program|-0.027|-0.026|
|Obs.|387|383|
|Professional program|0.027|0.026|
|Obs.|387|383|
|Annual tuition cost (in SOL)|1061.9|1460.4|
|Obs.|387|383|
|Program has high quality<br>accreditation|-0.002|-0.007|
|Obs.|387|383|
|Face-to-face program|0.033|0.030|
|Obs.<br>_Source_: Own estimations using WBSCPS.<br>_Notes_: This table summarizes the results from represe|387<br>ntativeness checks performed|383<br>using available program-|

_Notes_ : This table summarizes the results from representativeness checks performed using available programlevel administrative data, by country. In each column, we present estimated coefficients from the following model: yp = α0 + βDp + S+ εp for Brazil and Colombia and yp = α0 + βDp + εp for the rest of countries. In this model, yp is the characteristic of program _p_ (e.g., whether the program has high quality accreditation). We estimate a model for each characteristic available in the country’s administrative data. _S_ is the sampling strata variable, and Dp indicates a dummy that varies by model depending on the comparison. Column (1) presents the comparison of programs selected for the survey relative to the rest of non-selected programs within the universe of programs for which we have administrative information (Dp = 1 if the program was selected for the survey, and 0 otherwise). This check only applies to Brazil and Colombia, the countries for which we drew samples. Column (2) shows results from testing for differences between programs with an effective survey and all other programs in the universe. Column (3) is similar to column (2) but compares effective surveys with all other programs in the universe _that have not closed_ . All regressions estimate robust standard errors clustered at the HEI level. Significance levels: * p<0.1, ** p< 0.05, *** p<0.01.

**`**
# **Appendix 3:  Transition Matrices**

# **Table A3.1: Transition matrices including East European countries (Hodrick-Prescott filtered time-series)**

||**_Panel I:_**|**_Total numb_**|**_er of transit_**|**_ions_**|||**_Panel II_**|**_I: Mean firs_**|**_t passage ti_**|**_me_**||
|---|---|---|---|---|---|---|---|---|---|---|---|
||[0-0.15)|[0.15-0.3)|[0.3-0.45)|[0.45-0.6)|≥0.6||[0-0.15)|[0.15-0.3)|[0.3-0.45)|[0.45-0.6)|≥0.6|
|[0-0.15)|357|21|0|0|0|[0-0.15)|37.8|188.2|68.2|68.3|61.0|
|[0.15-0.3)|25|89|23|2|0|[0.15-0.3)|662.1|67.8|50.2|50.3|43.0|
|[0.3-0.45)|0|10|19|15|3|[0.3-0.45)|1292.0|170.2|144.7|29.1|20.5|
|[0.45-0.6)|0|0|3|16|11|[0.45-0.6)|1624.6|332.7|332.7|36.2|6.5|
|≥0.6|0|0|0|1|87|≥0.6|1712.6|420.7|420.7|88.0|1.1|
||**_Pa_**|**_nel II: Transi_**|**_tion matrix_**|||||||||
|p(i,j)<br>|[0-0.15)<br>|[0.15-0.3) <br>|[0.3-0.45) <br>|[0.45-0.6)<br>|≥0.6<br>|**_Ergodic distri_**|**_bution for tr_**<br>|**_ansition m_**<br>|**_atrix_**<br>|||
|[0-0.15)|0.94|0.06|0.00|0.00|0.00||0.03|0.01|0.01|0.03|0.93|
|[0.15-0.3)|0.18|0.64|0.17|0.01|0.00|**_Eigenvalues f_**|**_or transition_**|**_matrix_**||||
|[0.3-0.45)|0.00|0.21|0.40|0.32|0.06||0.22|0.56|0.75|0.98|1.00|
|[0.45-0.6)|0.00|0.00|0.10|0.53|0.37|**_Half-life for tr_**|**_ansition ma_**|**_trix_**||||
|≥0.6|0.00|0.00|0.00|0.01|0.99||35.4|||||

||**_Panel I:_**|**_Total numb_**|**_er of transit_**|**_ions_**|||**_Panel II_**|**_I: Mean firs_**|**_t passage t_**|**_ime_**||
|---|---|---|---|---|---|---|---|---|---|---|---|
||[0-1/16)|[1/16-1/8)|[1/8-1/4)|[1/4-1/2)|≥1/2||[0-1/16)|[1/16-1/8)|[1/8-1/4)|[1/4-1/2)|≥1/2|
|[0-1/16)|178|12|0|0|0|[0-1/16)|6.3|43.9|42.7|74.7|116.5|
|[1/16-1/8)|30|89|23|0|0|[1/16-1/8)|84.2|14.8|26.8|58.9|100.7|
|[1/8-1/4)|1|24|96|26|0|[1/8-1/4)|187.9|28.1|27.8|32.1|73.9|
|[1/4-1/2)|0|3|11|69|15|[1/4-1/2)|285.2|125.5|125.2|22.5|41.8|
|≥1/2|0|0|0|1|104|≥1/2|390.2|230.5|230.2|105.0|1.4|
||**_Pa_**|**_nel II: Trans_**|**_ition matrix_**|||||||||
|p(i,j)|[0-1/16)|[1/16-1/8)|[1/8-1/4)|[1/4-1/2)|≥1/2|**_Ergodic distri_**|**_bution for tr_**|**_ansition m_**|**_atrix_**|||
|[0-1/16)|0.94|0.06|0.00|0.00|0.00||0.16|0.05|0.04|0.04|0.72|
|[1/16-1/8)|0.21|0.63|0.16|0.00|0.00|**_Eigenvalues f_**|**_or transition_**|**_matrix_**||||
|[1/8-1/4)|0.01|0.16|0.65|0.18|0.00||0.44|0.63|0.86|0.99|1.00|
|[1/4-1/2)|0.00|0.03|0.11|0.70|0.15|**_Half-life for tr_**|**_ansition ma_**|**_trix_**||||
|≥1/2|0.00|0.00|0.00|0.01|0.99||53.4|||||

31
1. Experiment only: Only variables derived from the experiment modules that are administered following the household roster;

2. Experiment and non-experiment: Variables derived from the experiment modules as well as demographic, education and locational variables computed from the modules administered prior to the experiment modules; 3. WMS-linked poverty proxies as specified in NSO (2005)<sup>23</sup> ; and 4. Non-experiment only: Demographic, education and locational variables computed from the modules administered prior to the experiment modules.

Table 7 presents the differences in the predicted headcount poverty rates and Gini coefficients across different models and sample comparisons.<sup>24</sup> On the whole, variation in questionnaire design is sufficient to generate significant different estimates of both poverty and inequality. Using models 1 through 3, the predicted poverty rate based on the experiment sample is 3 and 7 percentage points _lower_ than the predicted poverty rate based on the standard sample (column 1). In all three cases, the predicted poverty rate based on the experiment sample is outside the estimated 95 percent confidence interval for the predicted poverty rate based on standard sample. Similar movements are observed in the predicted Gini coefficients, which are 3 to 4 percentage points _higher_ in the short experiment sample (column 1).

The differences in predicted Gini coefficients originating from models 1 through 3 is somewhat expected given the heterogeneity of short questionnaire impact highlighted during the discussion of Table 6. Models 1 through 3 based differences in predicted poverty rates and Gini coefficients also persist for same households as reported in column 2. Working with model 4 (i.e. only with the poverty proxies that are solicited prior to the variation in questionnaire design), there is only 1 percentage point difference in the predicted poverty rate and Gini coefficient between the experiment and standard samples, and the difference is no longer statistically significant. Moreover, looking at column 3, none of the differences between the predictions from the standard interviews of the non-experiment households and the predictions from the standard interviews of the experiment households are statistically significant. Hence, there is strong evidence that the variation in the predicted poverty and inequality statistics is related to the variation in questionnaire design underlying the poverty proxy definitions.

> 23 Three variables based on actual expenditures for cooking oil, sugar and soap are not included due to the need to rely on consumer price index series to adjust them over time. In private communication with Astrid Mathiassen, we were able to confirm that the exclusion of these variables from the WMS model does not affect the poverty predictions based on the annual WMS data from 2005 to 2008. We also exclude three binary variables on ownership of bed, iron and refrigerator due to the aforementioned issues in the data collection on durable asset ownership as part of the experiment.

> 24 The predicted poverty rates across scenarios are available upon request.

12
# **INTRODUCTION**

It is argued that carbon pricing allows reaching the internationally accepted temperature targets at minimal cost (Gollier and Tirole 2015; Borissov, Brausmann and Bretschger 2019). The report of the Commission on Carbon Pricing explains the role of education and skilled labor for the transition to green growth (Stiglitz et al. 2017). It explains that production using clean technologies is human capital intensive so that education decisions become closely interlinked with the energy transition. At the same time, shortages of skilled labor may constrain the decarbonization process of an economy (Faye 2012). The OECD (2017) argues that the path to a green economy can only be made by developing the right skills. Carbon pricing - by its impact on human capital accumulation - may not only be the cornerstone of climate policy but may also act as a development policy, fostering technology switches and education of the labor force (Borissov, Brausmann and Bretschger 2019). Yet, one mitigating factor that has received less attention is education quality. If technological change that reduces the reliance of production on emissions is skill-biased, then carbon pricing may increase the skill premium of earnings and subsequent wage inequality; however, a more elastic skill supply through better education quality may mitigate adverse economic outcomes including wage inequality and enhance the effect of carbon pricing on technological change and subsequently emissions. This paper explores the role of skills as measured through cognitive achievement on emissions through carbon pricing in European Union countries.

Many governments are enacting policies to place a price on greenhouse gas emissions, including the establishment of carbon taxes and cap-and-trade systems, to reduce emissions. For example, 31 high income OECD countries have one or more carbon pricing schemes, including 14 countries with carbon or emissions taxes and 25 countries with an emissions trading system (Yunis and Aliakbari 2020). According to the World Bank’s Carbon Pricing Dashboard (World Bank 2021), 27 countries have implemented a carbon tax nationally, and nine countries have implemented an emissions trading system; these figures exclude sub-national entities including provinces and states that have also implemented carbon pricing.

The economic impact of carbon taxes has been of considerable concern to policy makers, and a large body of literature has emerged on carbon taxes in particular since the early 1990s. The bulk 2
13 Analysis at the market level provides a policy-relevant metric to assess food system performance, and our analysis offers the added value of ensuring that the assessment is relevant for all types of individuals, and in realistic household settings.

Table 1 presents the characteristics of the households, household expenditure, markets, foods, and nutrients included in our sample. To establish the context for our affordability results, the median household already spends three-quarters of its resources on food and lives just above the international poverty line threshold of $1.90 per person per day in 2011 purchasing power parity (PPP) dollars (World Bank 2021).

<u>Table 1. Rural households and food markets in Malawi</u>

||20<br>|13<br>|2016<br>|/17<br>|Ove<br>|rall<br>|
|---|---|---|---|---|---|---|
||Mean|(SE)|Mean|(SE)|Mean|(SE)|
|Household size|4.76|(0.12)|4.98|(0.15)|4.90|(0.11)|
|Number of adults (>18)|2.32|(0.05)|2.52|(0.05)|2.44|(0.05)|
|Number of children (≤18)|3.51|(0.10)|3.52|(0.10)|3.51|(0.09)|
|Food items available per month per<br>market<sup>†</sup><br>|39.83|(4.94)|39.47|(4.59)|39.71|(4.82)|
|**_Household Expenditure (2011 US$_**<br>**_PPP)_**|Median|(SE)|Median|(SE)|Median|(SE)|
|Annual Food Expenditure|2,588.47|(113.63)|2,292.84|(94.08)|2,429.85|(91.74)|
|Per day (household)|7.09|(0.31)|6.28|(0.26)|6.66|(0.25)|
|Per day per capita|1.60|(0.10)|1.42|(0.06)|1.47|(0.05)|
|Annual Total Expenditure|3,460.88|(155.13)|3,319.20|(149.13)|3,354.48|(139.83)|
|Per day (household)|9.48|(0.43)|9.09|(0.41)|9.19|(0.38)|
|Per day per capita|2.29|(0.14)|1.98|(0.09)|2.10|(0.09)|
|Food Spending Share of Total|0.76|(0.01)|0.73|(0.01)|0.74|(0.00)|
|Expenditures<br>|||||||
|**_Observations_**<sup>†</sup><br>||N||N||N|
|Households<sup>*</sup><br>||1,424||1,693||3,117|
|Individuals<sup>‡</sup><br>||6,995||7,907||14,902|
|Markets<sup>⸸</sup>||25||25||25|
|Food items||51||51||51|
|Nutrients||22||22||22|

Population statistics corrected using sampling weights. †Standard deviation in parentheses. † Excluded: 260 infants under 6 months who are assumed to be exclusively breastfeeding, 4 rural households unable to be matched to a market. * 1,081 are unique households observed at both time points, however the composition of those households changes in January 2016, so these are best thought of as two consecutive, but separate panels of households and individuals. ‡ Excludes individuals who reported eating no meals in the household in the prior week, allowing diet cost to be compared to reported food consumption expenditure. ⸸ List of markets and districts provided in Supplementary Table A.
Formally, the model takes a generalized least squares form to estimate the joint distribution of per capita income _y_ in the household _h_ located in municipality _m_ , conditional on two sets of covariates: _Xhm_ , which includes household and individual characteristics, and _Zm_ , which includes fixed characteristics of the municipality of residence. The parameter _α_ is a household-specific effect; _β_ and _γ_ are the correlation parameters between the corresponding sets of covariates and _ln_ ( _yhm_ ); and, _μhm_ = _ηm_ + _εhm_ represents an error term, where _ηm_ is the component that is common to all households located in the same municipality (assumed to be homoscedastic and _i.i.d._ ), and _εhm_ is the component that is specific to each household (assumed to be heteroscedastic because it depends on the characteristics of the household and the municipality). The estimates of _β_ , _γ_ , and _μhm_ are then applied to the corresponding sets of covariates _Xhm_ and _Zm_ in the census to simulate, using the bootstrap method, the distribution of household per capita income.

The empirical support of extensive applications has shown this methodology to be robust (Alderman et al. 2002; Bedi et al. 2007). It has become common practice to use the methodology to improve targeting in developing countries facing the dilemma between precision and geographical detail. In Mexico, the small area estimation technique has already been applied to map income-based poverty officially across municipalities, but only in 2000 and 2005.<sup>4</sup> This paper relies on data on five points in time over a 22-year period by pairing available rounds of the Household Income and Expenditure Survey (ENIGH) and censuses collected in or around the same years (1990–92, 2000, 2005, 2010, and 2014–15).<sup>5</sup> At each point in time, the survey is a random sample of the corresponding census sample frame.<sup>6</sup> This allows strict comparability of the distributions of a given variable between both data sources. Even in pairings where gaps exist, that is, 1990–92 and 2014–15, it is possible to identify common sets of covariates _Xhm_ that satisfy the equality criterion, mainly because

> 4These exercises were conducted by CONEVAL by simulating income in the population censuses of 2000 and 2005. Though new rounds of census data were available in 2010 and 2015, the estimates of poverty followed a multidimensional approach based on a different methodology. Thus, a longer series of comparable income-based poverty data across municipalities is not officially available.

> 5The census data correspond to the general census of population and housing for the years ending in zero; for 2005, the data are taken from the population and housing count; and, for 2015, they are taken from the intercensal survey. Unless otherwise stated, from here onward, the term census refers indistinguishably to these three data sources.

> 6In 2014–15, both the ENIGH and the intercensal survey represented random samples of the 2010 general census sample frame.

8
firms are somewhat more similar to male entrepreneur firms in being larger and having a lower share of skilled workers. With larger sizes and better access to finance, male entrepreneur firms are likely to have better chance to compete with larger FDI firms. Maleled firms presumably would also have stronger political connections and power (Goldstein and Udry, 2008; Miao et al., 2019), and they are likely better able to deal with mounting competition of large foreign-owned firms in the same industry.

To shed light on how industry-level FDI premium is related to female entrepreneurship, we compute the average of firms’ foreign ownership share and the share of female entrepreneur firms for each country-industry-year cell, with both subtracting the countrylevel mean values. De-meaning both variables has the effect of controlling for the country fixed effects. We plot the country-industry level correlations between foreign ownership and the share of female entrepreneurship. As Figure 2 shows, the relationship between industry-level foreign ownership (relative to the country) and female entrepreneurship is not systematically positive or negative. Whether the results hold once we control for confounding variables, we shall leave it to multivariate regressions, to which we now turn.

# **5. Multivariate regression analysis**

We allow female entrepreneurship ( _Female Entrep_ ) of domestic firms _i_ of country _c_ and industry _j_ to be affected by FDI through two different channels. The first is the overall level of FDI inflow to the corporate sector, as captured by the average foreign ownership of all sample firms in country _c_ , and we call this . The second component is the industry FDI ( c ), as captured by subtracting from the average of corporate foreign ownership of cc FF firms in country _c_ and industry _j_ . We allow FDI to have industry component to take into cc FF FF account intra-industry spillover effects and the potential crowding out effects. Our first specification is the following: F F F E E c c (1) iic ii 2cc cc 1cc cc iic ii cc ii iic ii Here, is country-level control variables including the GDP per capita, education (as FFEE = ββ ZZ + ββ FF + δδ FF + XX ββ+ θθ + γγ + εε measured by the index of higher education and training, from World Economic Forum), cc ZZ and the urbanization rate. is a vector of firm-level controls and local business environment. Firm-level controls include the ownership share of the largest owner (as a iic ii XX 11
_Appendix Table 11: Program impact on health seeking behavior of beneficiary households_

||_Preg_|_nant women/lastp_<br>Timel|_regnancy in th_<br>Content of|_e last 36 month_<br>Postnatal|_s_|_Children_|_younger than 6_|_0 months old_|
|---|---|---|---|---|---|---|---|---|
||Any antenatal<br>care|y<br>initiation of<br>ANC|<br>care in<br>ANC|<br>care within<br>24 hours|Institutional<br>Delivery|MMR|Growth<br>Monitoring|Treatment<br>seeking|
|_Panel A: Program impact_|||||||||
|Program village|0.039<br>(0.031)|0.107**<br>(0.049)|0.719***<br>(0.201)|0.102**<br>(0.049)|0.018<br>(0.048)|0.056<br>(0.039)|0.152***<br>(0.032)|0.134***<br>(0.039)|
|_Panel B: Interaction with sat_|_uration_||||||||
|Program village|-0.052|0.440**|1.133|0.235|-0.125|-0.072|0.321***|0.043|
||(0.104)|(0.175)|(0.694)|(0.172)|(0.143)|(0.121)|(0.110)|(0.139)|
|Saturation|<br>-0.450***|<br>0.376|<br>-2.485***|<br>-0.657***|<br>-0.873***|<br>-0.260|<br>0.047|<br>-0.401**|
||(0.173)|(0.232)|(0.960)|(0.198)|(0.199)|(0.167)|(0.113)|(0.179)|
|Program|0.144|-0.526**|-0.613|-0.214|0.226|0.204|-0.275*|0.142|
|<br>village*saturation|(0.193)|(0.265)|(1.132)|(0.252)|(0.222)|(0.198)|(0.165)|(0.207)|
|_Panel C: Interaction with ab_|_ove median satura_|_tion_|||||||
|Program village|0.014|0.166**|0.716***|0.124*|-0.020|0.034|0.179***|0.070|
||(0.030)|(0.071)|(0.261)|(0.067)|(0.070)|(0.050)|(0.045)|(0.057)|
|Above median saturation|-0.072|0.101|-0.783***|-0.072|-0.187**|-0.001|0.051|-0.131**|
||(0.052)|(0.086)|(0.296)|(0.073)|(0.074)|(0.070)|(0.053)|(0.065)|
|Program village*above|<br>0.046|<br>-0.110|<br>0.004|<br>-0.047|<br>0.065|<br>0.048|<br>-0.053|<br>0.127|
|median saturation|(0.060)|(0.102)|(0.400)|(0.094)|(0.100)|(0.080)|(0.070)|(0.078)|
|_Panel D: Interaction with to_|_p quartile of satura_|_tion_|||||||
|Program village|0.029|0.169***|0.762***|0.154***|0.029|0.044|0.195***|0.115**|
||(0.028)|(0.057)|(0.209)|(0.055)|(0.059)|(0.044)|(0.039)|(0.048)|
|Fourth quartile of|<br>-0.083|<br>0.213**|<br>-0.771*|<br>-0.094|<br>-0.148*|<br>-0.020|<br>0.070|<br>-0.036|
|<br>saturation|(0.063)|(0.093)|(0.400)|(0.081)|(0.080)|(0.081)|(0.051)|(0.066)|
|Program village*fourth|<br>0.033|<br>-0.201*|<br>-0.109|<br>-0.161*|<br>-0.031|<br>0.041|<br>-0.153**|<br>0.066|
|<br>quartile of saturation|(0.088)|(0.105)|(0.501)|(0.093)|(0.100)|(0.097)|(0.071)|(0.083)|
|Control observations|221|227|205|223|227|376|411|403|
|Treated observations|233|242|226|241|242|408|443|436|
|Control mean|0.910|0.410|4.932|0.296|0.291|0.436|0.175|0.409|

note: *** p<0.01, ** p<0.05, * p<0.1 Child level outcomes include quadratic terms for child age (in months), and all specifications include municipality fixed effects. Standard errors are clustered at village level; there are 130 villages. Source: 2011 _Pantawid_ impact evaluation survey 60