rafmacalaba/datause-encoder-data huggingface dataset:
regarding rebound effects that could reduce fuelwood savings or even increase fuel consumption. This task is left for the future.

If the results of the satisfaction survey are confirmed by measurements of use, the role of REDD+ purchase subsidies and payments could potentially be called into question. Our findings indicate that the cookstoves provide considerable benefits for the users themselves and many say they would buy and recommend the stove even without REDD payments. Additionality and eligibility for REDD+ payments therefore remain open to question.

We know, though, that fuelwood savings depend on human behavior and motivations and if _Mirt_ stove wood savings was a purely engineering matter we should observe savings of about 50% as advertised. Instead we find that 40% fuelwood savings compared with the traditional stove is at the 95<sup>th</sup> percentile and at the 5<sup>th</sup> percentile there is only 12% fuelwood savings. Behavior therefore seems to be important and we know that external benefits like carbon sequestration are under-provided if uncompensated. Careful thinking about the additionality of improved cookstoves is therefore a high priority.

We have shown that private benefits will be critical to mobilizing _Mirt_ stoves for climate change mitigation and it would be very useful to look into time-savings. The satisfaction survey suggests that the _Mirt_ stove can generate time-savings and most benefits go to women. Yet such findings should be placed in the context of the household labor budget. Do households really save 1-5 hours per week and what is the nature of those savings? The shadow value of time must also be evaluated in order to better understand whether this benefit is substantial for households.

# **References**

- Adkins, E., Tyler, E., Wang, J., Siriri, D., Modi, V. 2010. “Field testing and survey evaluation of household biomass cookstoves in rural sub-Saharan Africa. _“Energy for Sustainable Development_ 14, 172 - 185.

- Angelsen, A., Brockhaus, M., 2009. _Realising Redd+: National Strategy and Policy Options_ . Bogor, Indonesia: Center for International Forestry Research (CIFOR).

- Aprovecho Institute. 1984. _Fuel Saving Cookstoves,_ Published by GTZ. Braunschweig/Wiesbaden.

Arnold, J.E.M., Köhlin, G., Persson, R., 2006. Fuelwoods, livelihoods, and policy interventions: changing perspectives. _World Development_ 34 (3): 596–611 Bailis, 21 | P a g e
# **Appendix B: Fit of "after" model solution to 2007 data**

In line with the third stage of our methodology for estimating the aggregate and spatial economic impacts of China's NEN (see section 3.2), this appendix provides a more detailed consideration of the fit of the "after" numerical solution of our theoretical model to actual Chinese data for 2007. Table B1 reports a matrix of Pearson product moment correlation coefficients between equilibrium values for key endogenous variables associated with this solution (columns) and their observed empirical counterparts (rows). The diagonal elements of this matrix show that, for the 331 prefectures included in our analysis, the "after" numerical solution generates series for (nominal) urban wages, rural wages, income, income per worker and income per capita that are strongly positively correlated with their observed counterparts. Depending on the variable, the correlation coefficient ranges between + 0.7660 (for income per worker) and + 0.9295 (for nominal rural wages), implying that the "after" solution can account for between 60.21 % and 86.40 % of the actual observed variation in these variables.

**Table B1: Correlation between simulated values of key variables associated with "after" equilibrium** **<u>solution and actual data for 2007</u>**

|**Simulated /**<br>**Actual **|**_w_**<sup>**_U_**</sup>|**_w_**<sup>**_R_**</sup>|**_Y_**|**_Y pw_**|**_Y pc_**|
|---|---|---|---|---|---|
|**_w_**<sup>**_U_**</sup>|**0.8739**|0.7639|0.7410|0.8988|0.8983|
|**_w_**<sup>**_R_**</sup>|0.7034|**0.9295**|0.7362|0.9499|0.9189|
|**_Y_**|0.5874|0.5421|**0.9212**|0.6602|0.6164|
|**_Y pw_**|0.4083|0.6684|0.4613|**0.7660**|0.6320|
|**_Y pc_**|0.5409|0.7237|0.5558|0.8351|**0.7776**|

## **Notes:**

The reported correlation co-efficients are Pearson product moment correlation coefficients. All correlations are based on the full sample of 331 prefectures. _Y pw_ denotes income per worker; _Y pc_ income per capita - the observed values of these variables correspond to GDP per worker and GDP per capita respectively. Definitions of <u>all other variables are provided in Appendix A.1.</u> Figure B1, meanwhile, compares the simulated distribution of urban-rural wage ratios associated with the "after" equilibrium solution,  _After_ , with the actual observed distribution for 2007. In general, the correspondence between the two distributions is good, although actual urban-rural wage ratios are more dispersed. This is a consequence of a slight tendency for the equilibrium solution to over-predict (under-predict) urban-rural wage ratios where they are low (high).

41
covariates and pairs green and non-green contracts achieving a high degree of similarity across the two groups and hence minimizing confounding due to observed covariates (Iacus et al, 2012). The covariates used for matching are

- Buyer type (e.g., central government body, regional/local entity)

- Contract type (goods/works/services)

- Product market (2-digit CPV code)

- Buyer location (NUTS3)

- Supplier location (NUTS3)

- Tender year.

In the second step, we run regression models on the matched samples, including control variables:

- Contract value (categorized as deciles)

- Buyer size: total value of contracts awarded annually.

Individually and taken together, these control variables account for observable contract and organization characteristics that are likely to impact public procurement outcomes such as competition and corruption risks, but also on the use of GPP. For example, green contracts may be more frequent in larger markets where suppliers may also face more bidders. Such market characteristics are accounted for by including buyer and supplier location, buyer size and product market category. Similarly, larger tenders may be more amenable to green procurement as they are able to absorb higher investment costs of using new technologies. This is controlled for by including contract value in the regressions.

The comprehensive information on public procurement transaction data in Bulgaria is thus critical to identify the impact of green public procurement on procurement and firm outcomes.

The second modeling approach tackling challenges to causal identification we deployed is interacted fixed effects - market * year - regressions on the contract level, including the same set of control variables as for matching. The extensive set of fixed effects are designed to capture a wide range of unobserved variables, in addition to controlling for observable confounders. In essence, the fixed effects regressions allow us to identify the impacts of GPP in very narrowly defined spaces, within any particular combination of market-years. By implication, our impact estimates should be interpreted as within market changes while also controlling for any time-dependent external shocks. Looking at matched regressions and fixed effects regressions in tandem allows us to put forward complementary solutions to identifying casual effects. The matching allows for a tighter comparison on observables while the fixed effects regressions account for a variety of 14
# **Table 1. Background Characteristics**

|**Panel A. Background Information**<br>Age|28.43|||
|---|---|---|---|
||(6.98)|||
|Age Began Seeing Clients|18.67<br>|||
||(5.14)|||
|Educational Attainment|9.20<br>|||
||(2.69)|||
|Literacy|_Read Kiswahili_<br>|_Write Kiswahili_<br>||
||0.95|0.88||
|Respondent is Head Of Household|0.84|||
|Total # of Dependents|2.96<br>|||
||(2.36)|||
|Respondent is in a Peer Group|0.44|||
|Number of Regular Clients (at time of|2.24|||
|background survey)<br>|(1.07)|||
|Marital Status||||
|_Widowed_|0.23|||
|_Divorced / Separated_|0.20|||
|_Cohabitating_|0.13|||
|_Never Married / Not Cohabitating_|0.44|||
|Observations|192|||
|**Panel B. Labor Supply in the Sex Market(Daily Averag**<br>Participated in Sex Market|**es)**<br>0.76|||
|Total Income from Sex Work|686.84|||
||(749.55)|||
|Total Income from All Sources|788.26<br>|||
||(778.89)|||
|Number of Clients Seen|1.52|||
||(1.12)|||
|Number of Regular Clients Seen|0.54<br>|||
||(0.66)|||
|Had Unprotected Sex|0.18|||
|# of Times Unprotected Sex|0.42|||
||(1.10)|||
|Observations<br>|12526<br>|||
|Number of Women|192|||
||||_Occurred at Least_|
|||_Prob.of Occurring_|_Once over 3 Month_|
||_Daily Average_|_in a Week_|_Sample Period_|
|**Panel C. Shocks**|<br>(1)|<br>(2)|<br>(3)|
|Someone in Household Sick (other than respondent)|0.37|0.60|0.93|
|Respondent Sick|0.34|0.60|0.98|
|A Friend or Relative of Respondent Died|0.04|0.14|0.46|
|Respondent was Asked for Money|0.07|0.23|0.70|
|Respondent had STI|0.03|0.08|0.34|
|Observations|12481|2384|209|
|Number of Women|192|192|192|

Notes: Figures in Panel A are averages across the 192 women in the sample, from a background survey. Figures in Panels B and C are daily averages from self-reported diaries. There are more observations than IDs in Panel C, Column 3 because 17 women took part in 2 rounds of diaries. Sickness is an indicator variable equal to 1 if the household or respondent reported having a cough, fever, malaria, typhoid, diarrhea, cuts or burns, or any other illness.

The exact number of observations differ for some variables due to missing values. Exchange rate was roughly 70 Kenyan shillings to US $1 during the sample period. Standard deviations in parentheses.
propensity score matching estimators to control for a variety of observed characteristics measured at the municipality, barangay<sup>2</sup> , household and child levels and unobserved fixed characteristics. We examine intent-to-treat impacts by child age, and because program implementation was not instantaneous, we also consider the length of exposure to the program. Our results indicate that the program has had important positive impacts on children’s cognitive, motor, language and social development and short-run nutritional status, with some suggestion that duration of exposure increases the program impacts. The effects are particularly important for younger children (below four-years-old at the time of the evaluation).

# **Evaluations of ECD programs**

There have been few systematic evaluations of ECD programs in developing countries that also use longitudinal data to control for the behavioral aspects of investments in preschool human capital; three are discussed later below. However, several programs in the United States that have been evaluated provide important lessons. The High/Scope Perry Preschool Study (Michigan) is a study of the effects of a high-quality preschool program for children born in poverty. It randomly assigned 123 children to program or noprogram control groups, and followed these children with respect to a variety of outcome measures from age 3 to age 41. From October through May each program year, program teachers conducted daily 21⁄2-hour classes for children on weekday mornings and made weekly 11⁄2-hour home visits to each mother and child on weekday afternoons. Program evaluations have found gains in children’s readiness for school and their subsequent educational success, economic success in early adulthood (even home ownership), and reduced number of criminal arrests throughout their lives (Schweinhart and Weikart 1998; Belfield and others 2006). In addition to direct benefits to participants, the general public gains also in terms of higher tax revenues, lower criminal justice system expenditures, and lower welfare payments, repaying $12.90 for every $1 invested, easily outweighing program costs. In contrast to the Perry program, the Home Instruction Program for Preschool Youngsters (HIPPY) is exclusively parent-focused; it is a two-year home-based early education intervention program that aims to help parents with limited formal education prepare their four- and five-year-old children for school (Baker, Piotrkowski and Brooks-Gunn 1998). Para-professional trainers visited families bimonthly, providing teaching materials and parenting advice, and supplemented the visits in alternate weeks by group meetings with parents and paraprofessionals led by professional HIPPY program coordinators. Experimental evaluations of HIPPY have found positive impacts on cognitive test scores, at least over a two-to-three-year interval after the program. Experimental evaluations of two other programs (e.g., the Milwaukee and Abecedarin ________________

> 2 A _barangay_ is the smallest political unit in the Philippines and is akin to a district or village.

2
day with 3.3 million lifted above the poverty line, followed by the FTAAP (2.1 million) and TPP15 (1.3 million). At the same time, the shape of the growth incidence curve also suggest that more ambitious agreements tend to concentrate larger gains in richer segments of the population, particularly in the case of the most ambitious of all, i.e. the EU-IDN CEPA (Figure 13a).

In contrast, RCEP and CPTPP (TPP-11) scenarios offer lower gains in terms of poverty reduction but better distributional outcomes. The RCEP scenario generates a reduction in poor people by 0.3 million for the PPP$3.20 a day line and by 0.8 million for the PPP$3.20 a day line **(** Figure 14b). The growth incidence curve (Figure 13b) shows that CPTPP might have a negative impact on the middle-class, sending more people - 0.3 million -below the poverty at the PPP$5.50 a day.

# **Figure 13. Growth incidence curves in simulated scenarios in 2030.**

<!-- Start of picture text --> 13a. EU-IDN, FTAAP and TPP-15 scenarios 13b. RCEP and CPTPP scenarios<br><!-- End of picture text --> <!-- Start of picture text --> Growth Incidence Curve with respect to Baseline, 2030<br>0 20 40 60 80 100<br>Percentiles of Per Capita Income<br>EU-IDN FTAAP TPP-15<br>3<br>2<br>1<br>% change, w.r.t. Baseline<br>0<br><!-- End of picture text --> <!-- Start of picture text --> Growth Incidence Curve with respect to Baseline, 2030<br>0 20 40 60 80 100<br>Percentiles of Per Capita Income<br>RCEP TPP-11<br>3<br>2<br>1<br>% change, w.r.t. Baseline<br>0<br><!-- End of picture text --> 26
<!-- Start of picture text --> o<br>o<br>oa<br>fa] eg ty Sui agba a<br>ES - os ae<br>= | Bes pi-023<br>3 - 1HJi . eee a<br>o .<br>-6 4 2 oO 2 4<br>PISA index of economic, social and cultural status<br><!-- End of picture text -->
# **Table 9: Cross-country covariates associated with loan penetration**

The results of OLS regressions of the natural logarithm of number of outstanding loans per 1,000 adults in commercial banks on different country characteristics.

||(1)|(2)|(3)|(4)|(5)|(6)|(7)|(8)|(9)|(10)|(11)|
|---|---|---|---|---|---|---|---|---|---|---|---|
|GDP per capita (log)|0.951***<br>(0.084)|0.954***<br>(0.083)<br>|0.778***<br>(0.106)|0.904***<br>(0.089)|0.959***<br>(0.090)|0.785***<br>(0.080)|0.572***<br>(0.156)|0.428***<br>(0.131)|0.880***<br>(0.083)|0.940***<br>(0.086)|0.980***<br>(0.100)|
|Population density (log)||-0.029<br>(0.055)||||||||||
|Branches per adult (log)|||0.388**<br>(0.162)|||||||||
|Branches per km<sup>2</sup>(log)||||0.061<br>(0.050)||||||||
|Expected real economic growth|||||0.150<br>(0.377)|||||||
|Inflation||||||-0.049**<br>(0.019)||||||
|Electricity consumption|||||||0.335<br>(0.225)|||||
|Land line and cell phone users||||||||1.102***<br>(0.198)||||
|Absence of violence|||||||||0.091<br>(0.133)|||
|Offshore financial center||||||||||0.309<br>(0.221)||
|Lending rate|||||||||||0.016<br>(0.012)|
|Ctt<br>|-2.716***|-2.615***|2.255|-2.006**|-2.916***|-0.786|-1.903*|-3.191***|-2.080***|-2.649***|-3.137***|
|onsan|(0.714)|(0.777)|(2.137)|(0.789)|(0.934)|(0.761)|(1.000)|(0.638)|(0.692)|(0.726)|(0.885)|
|N<br>|68|68|67|65|68|63|56|51|66|68|53|
|Adjusted R<sup>2</sup>|0.760|0.757|0.792|0.737|0.757|0.779|0.673|0.858|0.734|0.759|0.722|
|Robust standard errors in parentheses<br>***p<0.01,**p<0.05,*p<0.1||||||||||||

32
reference year is 2004. For the estimation of trade flows, the authors used data on transport and economic data on exports and imports (Canning and Tsigas, 2000; Dubé and Lemelin, 2005) to overcome the lack of data. Then, a cross-entropy optimization method is applied to make the information inside the model consistent. Finally, the trade flows across subnational regions are adjusted by the RAS statistical method to increase the consistency of transportation flows with the production data.

Cherubini and Los (2016) follow a macro-oriented approach, introducing the results of a first attempt to integrate sub-national input-output tables for Italy into the World InputOutput Database (WIOD).<sup>2</sup> In this way, they assess the effects of changes in the structure of value chains on regional employment patterns. The regionalization is applied by considering 4 Italian macroregions (Northwest, Northeast, Centre, and South and Islands). The approach adopted to integrate the Italian IRSUTs into the WIOD system of international SUTs was to replace the tables for Italy as a whole with 4 Supply and 4 Use tables, a couple for each region (the "Four Italies"). As a result, the final database, where each of the "Four Italies" is a country itself, has 44 countries (comprehensive of the "rest of the world") instead of the original 41. Firstly, for each product, the interregional imports and imports from abroad have been divided into intermediate consumption, and capital use of goods and services, according to the proportions observed for the imports in the international Use for Italy as a whole. The international transport margins (ITMs) of each of the four regions when trading goods with foreign countries, as they result from the international WIOD tables, have been distributed according to the proportion of the total regional imports from abroad of each product.

More recent works for Italy are contained in Gesualdo et al. (2019) and Severini et al. (2019). The first (Gesualdo et al., 2019) presents Italy’s multisectoral computable general equilibrium tax model. It shows a methodology for modeling and accommodating the full range of direct and indirect taxes. The authors provide a powerful tool for acquiring new insights into fiscal policy analysis through a commodity tax matrix and a production tax matrix. Simulations are performed to evaluate a value added tax rates reform, finding that a budget-neutral uniform tax rate reform would improve GDP and welfare. The second paper (Severini et al., 2019) focuses on gender disparity in Italy, showing another powerful aspect of CGE models. The authors present a multisectoral analysis to bridge gender disaggregation within income formation. A gender-aware SAM is used to evaluate the impact of different fiscal policies to reduce female labor costs.

> 2The WIOD database provides annual time series of world input-output tables (WIOTs) from 1995 to 2011, including 35 industries and 59 products.

5
extensively estimated the relationship between “technology shocks” and total hours worked in the United States and some European economies, finding a negative impact on total hours worked in the first year following the shock (Gal ́ı, 1999; Francis and Ramey, 2005; Francis et al., 2014).<sup>4</sup> In this paper, innovations in technology (“technology shocks”) are identified as shocks that drive the largest proportion of long-run developments in labor productivity, while other shocks are assumed to have only transitory effects. Therefore, technology shocks represent any structural development that results in lasting changes in labor productivity. These innovations could boost labor productivity through higher technical efficiency, in the sense of higher TFP in a standard Cobb-Douglas framework. Equally, new technologies may allow for the replacement of tasks previously completed using manual labor with capital, or “robot” inputs, altering the ratio of labor and capital inputs, without necessarily boosting the overall efficiency with which capital and labor are combined (Acemoglu and Restrepo, 2018). In both cases, output per unit of labor input is increased. The identification may capture a mix between labor-augmenting and labor-displacing technologies.

Traditionally, long-run restrictions have been used to identify technology shocks. However, longrun restrictions are susceptible to significant bias from the presence of non-technology shocks which can also drive developments in productivity (Chari et al., 2009; Dieppe et al., 2019, 2021). Instead, the Spectral identification, detailed below, is more robust, particularly when applied to volatile emerging and developing market economy data.

# **3.1 Spectral identification of technology shocks**

The Spectral identification searches for the shock which maximizes the contribution to the variance of productivity at long-run frequencies. This abstracts from the shocks which may instead drive variation in labor productivity at business-cycle frequencies, such as demand and other short-run impact shocks. This approach effectively applies a band-pass filter to the reduced-form coefficients of a VAR containing macroeconomic variables, identifying the spectral density of the variables within a particular frequency band. The technology shock is then identified by maximizing the variance of labor productivity explained at the desired frequency.

We start by writing the Wold representation of the VAR (assuming it is invertible): Here, _B_ are the reduced-form auto-regressive coefficients, while _u_ are the reduced form errors in the VAR representation and _D_ reflects the sum of the MA-representation coefficients of the VAR. By post-multiplying _Yt_ by _Yt−τ_ and summing across its lags (of _τ_ periods), the series of auto and cross covariances ( _γ_ ) can be written as a function of _D_ and the variance-covariance matrix of errors:

> 4For a detailed review of the technology SVAR literature, see Ramey (2016).

7