rafmacalaba/datause-encoder-data huggingface dataset:
## _4.3 Determinants of Fuelwood Savings_

We now present our analysis of fuelwood savings by combining household survey and CCT data. We employ robust pooled OLS, with errors clustered at the village level. We provide regression estimates with and without treatment variables. Results are presented in Table 10, with the dependent variable fuelwood savings (i.e., the difference in fuel consumption between traditional and _Mirt_ stoves normalized by 100).

We include both linear and squared weight of final food cooked (i.e. weight of the _injera_ baked) to control for possible nonlinear scale effects and results suggest significant diseconomies of scale of the _Mirt_ stove. At the mean the first order elasticity of fuelwood savings with regard to food cooked is about -3.94.We also find that older, married cooks who primarily do non-household work enjoy more savings from the _Mirt_ stove than others and that cooking under calm wind condition results in more fuelwood savings. We control for cooking time, because slow cooks may use more wood, but find no effect. Regional state also appears to be a significant determinant of fuelwood savings. Households in SNNP save less fuelwood than those in Oromiya and Amhara.

We analyze the effect of the randomized treatments on fuelwood savings by including dummy variables for each treatment, with treatment 5 (no payment, no use incentive paid and no network aspects) as the omitted category. Only treatment 2 has a statistically significant effect (positive), suggesting that providing incentives for using improved stoves along with network building and group training may increase fuelwood savings. That said, that other treatments have no statistically discernable effects suggest that the behavioral treatments are not important for per meal fuelwood savings.

# **5. Conclusions and Implications for REDD+**

This paper analyzes the role that biomass cookstoves designed to be cleaner and more fuel-efficient might play in a REDD+ program through reduced fuelwood use and increased carbon sequestration. We utilize random samples from Ethiopia regional states that include about 80% of the country’s population of over 90 million. We therefore believe our results are representative of and applicable to most of this very important country. In addition to contributing to the academic literature on the fuelwood savings of 17 | P a g e
|can be skilled<br>expatriates.<br>Ireland:|of 10 skilled expatriates.|exempted from the<br>65,000 figure. There are<br>no quotas for O-1 visas<sup>6</sup>.|
|---|---|---|
|Maximum 50% of<br>employees can be<br>non-EU citizens.||Italy: The Quota<br>Agreement ("decreto<br>flussi") for 2011 is 86,580<br>for workers without<br>permit of stay in Italy; 11,<br>500 for workers with<br>Italian permit of stay for<br>other reasons<sup>7</sup>.|

Source: FDI Regulations Database

# **4.2 The ease of hiring skilled expatriates**

This indicator measures the time and procedural steps required to legally start working in the host country on a TWP. For ease of reference this period will be referred to as “time to obtain a TWP”.

# **4.2.1 What is specifically measured when measuring the “time required to obtain a TWP”?**

The time required to obtain a TWP measures the number of calendar weeks it takes for a skilled expatriate (IT specialist) to comply with all requirements of an economy after which s/he is legally allowed to start working. The details of each step are provided in Figure 5.

The starting point of the time period measured is when the company has a need for a foreign IT specialist and has identified a suitable candidate who they want to hire (Step 1). Our case study states that the company is a newly established company which previously has not hired foreign expatriates.

In some economies, companies are required to obtain a certification before they are able to hire foreign expatriates (Step 2). Step 3 entails all procedures which the company and/or employee need to comply with in order to legally start work. The end point of the measured time period (Step 4) is the moment when the foreign IT specialist is legally allowed to start working.

> 6 An O visa is a classification of non-immigrant temporary worker visa granted by the United States to an alien "who possesses extraordinary ability in the sciences, arts, education, business, or athletics, or who has a demonstrated record of extraordinary achievement in the motion picture or television industry and has been recognized nationally or internationally for those achievements," and to certain assistants and immediate family members of such aliens.

> 7 There is no distinction between specialized and non-specialized workers. The application scope of the quota depends of the nationality of the worker and industry sectors. The quota's decree is binding if the foreign worker does not have a permit to stay in Italy. Quotas are calculated on the basis of market requests.

10
Table 2. Women's Status and Household Factors, Nepal DHS, 1996-2006

||**1996**<br>_Unweighted N_|_% of Sample,_<br>_Weighted_|**2001**<br>_Unweighted N_|_% of Sample,_<br>_Weighted_|**2006**<br>_Unweighted N_|_% of Sample,_<br>_Weighted_|
|---|---|---|---|---|---|---|
|Overall Sample|8373|100.0|8719|100.0|8632|100.0|
|**Basic indicators of women’s status**|||||||
|Employed|||||||
|Yes|6634|77.3|7341|82.9|6481|73.6|
|No|1739|22.7|1378|17.1|2151|26.4|
|Husband Gone|||||||
|Yes|1747|20.8|2096|25.0|2657|29.7|
|No|6626|79.2|6623|75.0|5975|70.3|
|Education|||||||
|No schooling|6689|80.0|6265|72.0|5371|62.6|
|Some or all primary school|890|11.0|1272|14.8|1461|16.8|
|Some secondary school|537|6.3|832|9.3|1211|14.1|
|Completed secondary school +|257|2.7|350|3.9|589|6.4|
|Literate|||||||
|Yes|1802|20.9|3134|35.3|4072|46.7|
|No|6571|79.1|5585|64.7|4560|53.3|
|Age|||||||
|age<=20|1275|15.5|1218|14.4|1061|12.1|
|20<age<=35|4467|53.4|4744|54.2|4646|53.4|
|age>35|2631|31.2|2757|31.4|2925|34.5|
|**Geographical indicators**|||||||
|Region|||||||
|Eastern|1666|22.9|2067|24.1|1916|21.4|
|Central|2489|33.4|2388|32.1|2213|33.2|
|Western|1584|19.6|1556|20.3|1683|19.4|
|Mid-Western|1389|14.3|1141|13.7|1403|11.7|

33
important aspect of the runoff regime to simulate correctly. A genetic mutation algorithm (Frontline Systems Inc. 2003) was used to optimize the parameters. This approach enables the whole of the ‘solution space’ to be searched and generally allows solutions to be obtained more rapidly than alternative optimization methods. The AWCmult parameters were constrained to lie within a range 0.1m to 5.0m depending on the likely rooting depth of vegetation within the specified land use class (Table 3).

Using a dedicated computer, parameter calibration took approximately 15 seconds per grid cell. Thus the total time for calibrating parameters for all cells in Africa was three days. A comparison between average annual runoff generated using WatBal and the UNH runoff data, at the scale of ‘food production units’ (of which there are 97 in Africa) shows a reasonable fit (Figure 8).

# **_2.4 Runoff simulation_**

When the model was calibrated it was run to estimate runoff, on a monthly time step, for all grid cells for the period 1961–1990. The cell data were then converted to runoff in each district. The percentage of each district’s area in each associated grid cell was determined using a blank global 0.5 x 0.5 degree grid and geoprocessing tools in ArcView. This enabled the weighted average runoff produced by the grid cells contributing to runoff within any given district to be determined. For example, at any given time step, all districts in the same grid cell will have the same runoff. However, if a district was divided across a number of grid cells (as illustrated in Figure 9), runoff for that district was calculated based on the percentage area of the district in each of the grid cells.

# **_2.5 Flow simulation_**

The flow routing model uses the STN-30p drainage network (Section 2.2.3) shown in detail for Burkina Faso in Figure 10. The drainage network shows the direction of flow in each grid cell and which upstream cells are contributing to its flow. Runoff from each grid cell is accumulated along the drainage network, providing the stream flow in each grid cell (Figure 11). After stream flow had been calculated in each grid cell, the same weighted average 15
reports that it analyzes big data via either of the following methods: the enterprise’s own data collected with smart devices or sensors, data gathered from geolocation data from the use of portable devices, generated from social media, and data collected from other external sources.<sup>22</sup> The E-commerce survey includes information on two separate measures of AI technologies, notably machine learning and natural language processing. Similar to the construction of the other technology variables, we create a binary variable equal to 1 for firms using AI in either of these forms and zero otherwise.

The matched employer-employee data comes from the Annual Survey of Hours and Earnings, provided by the ONS, and comprises panel data on the wages of 1% of UK workers. Since the data is a sample of workers, this prevents us aggregating to the firm-level, and limits us to worker-level analysis. We examine the demand for workers that perform data analytics (or business intelligence) tasks , those that perform other data tasks (specifically, database, data stores or software creation) and those that do not perform data tasks, using occupation information on time use from Corrado et al. (2022).<sup>23</sup> Details on the Annual Investment Allowance policy over time are provided by UK Tax Authority (HMRC). This data contains information on investment thresholds of the allowance, eligible investment, when the policy was introduced (2008) and details on changes in the thresholds over time up to 2019. Measures of IT capital investment, as well as historic (pre-AIA) total investment in plant and machinery and the date of each firm’s financial – – year end which we use to identify our set of treated firms are taken from the Annual Business Survey (provided by the ONS). Finally, data on firm control variables, age, multi– establishment status and foreign ownership are sourced from the UK business registry the annual Business Structure Database.

> 22The E-commerce survey defines big data and big data analytics as the following. Big data typically have characteristics such as: (1) vast amounts of data generated over time, (2) variety in terms of different formats of complex data, either structured or unstructured (for example text, video, images, voice, docs, sensor data, activity logs, click streams, coordinates). (3) velocity in terms of the high speed at which data are generated, become available and change over time. Big data analysis refers to the use of techniques, technologies and software tools for analyzing big data from our own business or other data sources.

> 23Given that half of workers do not spend any time on data tasks, see Table A2, we use non-zero time use on data tasks to determine these groups

12
Table 1: Savings Functions for the Panel Data of 74 countries, 1960-90 Dependent Variable: Savings rate (t-Statistics in parenthesis)

||(1)|(2)|(3)|(4)|(5)|(6)|(7)|(8)|
|---|---|---|---|---|---|---|---|---|
|GDP per capita growth|4.255<br>_(3.98)_|5.217<br>_(5.99)_|4.558<br>_(4.37)_|5.412<br>_(6.46)_|4.637<br>_(3.95)_|5.523<br>_(5.86)_|4.495<br>_(3.77)_|5.674<br>_(6.04)_|
|Lagged GDP growth per capita||1.922<br>_(1.94)_||1.956<br>_(1.99)_||1.979<br>_(1.87)_||1.239<br>_(1.30)_|
|Dependency ratio|-0.566<br>_(-3.42)_|-0.378<br>_(-2.74)_|-0.409<br>_(-2.78)_|-0.266<br>_(-2.21)_|-0.552<br>_(-2.84)_|-0.357<br>_(-2.20)_|-0.535<br>_(-2.41)_|-0.317<br>_(-1. 74)_|
|Lagged Savings Rate||0.287<br>_(3.36)_||0.290<br>_(3.35)_||0.292<br>_(3.15)_||0.322<br>_(3.51)_|
|Overall Education Stock|-1.848<br>_(-3.67)_|-1.625<br>_(-3.30)_|||||||
|Lagged Overall Education Stock|1.508<br>_(3.62)_|1.395<br>_(3.39)_|||||||
|Primary Education Stock|||-1.832<br>_(-3.68)_|-1.688<br>_(345)_|||||
|Lagged Primary Education Stock|||1.489<br>_(3.61)_|1.429<br>_(3.50)_|||||
|Secondary Education Stock|||||-0.846<br>_(-2.67)_|-0.580<br>_(-2.20)_|||
|Lagged Secondary Education Stock|||||0.726<br>_(2.70)_|0.517<br>_(2.26)_|||
|Terbary Education Stock|||||||0.013<br>_(0.13)_|-0.006<br>_(-0.07)_|
|Lagged Tertiary Education Stock|||||||-0.039<br>_(-0. 54)_|-0.001<br>(-0.01)|
|AdjR2<br>DWa|0.153<br>1.88|0.301<br>2.11|0.151<br>1.87|0.303<br>2.15|0.143<br>1.85|0.283<br>2.11|0.112<br>1.89|0.272<br>2.10|
|Observations|369|369|369|369|369|369|369|369|

All variables are expressed in log (with the exception of the GDP growth rate) and in 5 year average. The TSLS have been used in all regression and fxedeffects have been introduced to account for countries' characteristics.
- (2) The surveys were conducted no earlier than 2010. If there were multiple surveys available for the same country, we chose the most recent one. This allows us to provide a contemporary characterization of teacher pay across several African countries, especially when describing current levels and comparing those to other professions.

- (3) The survey data were accessible.

The resulting set of countries includes Côte d’Ivoire, Namibia, Sierra Leone, Tanzania and Zambia with data from national labor force surveys; Burkina Faso, Malawi, Niger, Nigeria and Uganda with data from Living Standards Measurement Surveys (LSMS) with an employment module; and the Democratic Republic of Congo, The Gambia, Ghana, Liberia and Senegal with data from national household surveys with a labor force module. Table 1 provides background information on these surveys: the countries, the source of the survey data and the year in which the survey was conducted.

The focus of our analysis is primary and secondary school teachers. This choice was driven by the fact that teachers in the basic education cycle make up the bulk of the teaching workforce. Additionally, their salaries are often set centrally and typically paid directly out of government expenditure on education, which makes cross-country comparison possible and meaningful. From each of these surveys, we identified primary and secondary teachers using occupation codes listed in interviewer manuals. Most of the surveys used the International Standard Classification of Occupations (ISCO) or a variant form of it. The occupation codes allowed us to differentiate primary and secondary teachers from other educators such as pre-primary teachers, special education teachers, and university lectures and professors. Exceptions are Malawi where the data classifies teachers at all levels in the same group. (Detailed data processing notes can be found in Appendix Table A1.) We retained Malawi in our analysis, but our conclusions are similar if we exclude it. We excluded educators other than primary and secondary teachers from our analytical samples since their jobs and pay determination may be quite different from primary and secondary teachers. Throughout this paper, “teachers” therefore only refers to primary and secondary school teachers.

Table 1 provides details about our samples: the number of teachers, the share of teachers in the formal sector, and the shares of teachers in the overall workforce in each survey. The number of teachers sampled ranges from 119 teachers in Niger to 1,104 teachers in the Democratic 6
Table 8: The Impact of HSR on Firms’ DVAR: The Role of Product Differentiation

|Rauch Classifcation|(1)<br>Conservative|(2)<br>Liberal|
|---|---|---|
|Dependent Variable|DVAR|DVAR|
|CI_×_Diff_t_0|-0.016***<br>(0.003)|-0.018***<br>(0.003)|
|CI|0.034***|0.033***|
||(0.007)|(0.006)|
|Control|Yes|Yes|
|Firm FE|Yes|Yes|
|City-Year FE|Yes|Yes|
|Observations<br>|197,307|197,307|
|Adjusted R<sup>2</sup>|0.723|0.723|

Notes: Robust standard errors clustered at the firm level are in parentheses, ***, **, * denote significance at 1%, 5%, 10% level, respectively. The firm-level control variables include input tariffs and import-weighted exchange rate variation (appreciation).

# **5.2 Extended Analysis: Impact of HSR on Exports and Imports**

Finally, to confirm that HSR expansion enables exporting firms to substitute imported materials with domestic alternatives without compromising their exports, Table 9 shifts the focus to export value, export variety, import value, and import variety. The results indicate that CI positively impacts exports and negatively impacts imports, reinforcing the idea that HSR gives exporting firms better access to domestic materials, which they use as substitutes for imported inputs. Table 10 examines the effects 6. of CI on export and import prices across firm types, similar to Table The findings show that export prices for FDI and processing exporters decline, while import prices remain unaffected by CI. This supports the hypothesis that HSR expansion allows these firms to replace imported materials with cheaper domestic inputs, leading to higher DVAR and lower export prices.

Table 9: The Impact of HSR on Firms’ Imports and Exports

|Dependent Variable|(1)<br>ln(Export Variety)|(2)<br>ln(Import Variety)|(3)<br>ln(Export)|(4)<br>ln(Import)|
|---|---|---|---|---|
|CI|0.159***|0.023|0.110**|-0.358***|
||(0.021)|(0.022)|(0.043)|(0.051)|
|Control|Yes|Yes|Yes|Yes|
|Firm FE|Yes|Yes|Yes|Yes|
|City-Year FE|Yes|Yes|Yes|Yes|
|Observations|286,794|286,794|286,794|286,794|
|Adjusted R<sup>2</sup>|0.834|0.808|0.760|0.767|

Notes: Robust standard errors clustered at the firm level are in parentheses, ***, **, * denote significance at 1%, 5%, 10% level, respectively. The firm-level control variables include input tariffs and import-weighted exchange rate variation (appreciation).

23
status of household member. The fact that we do not have individual level intake information precludes us from examining issues of inequality in food access within the household.

# **2.3 Related Literature**

This work shares similarities with a number of existing efforts. The earliest work in this area was naturally focused more on whether or not caloric adequacy is sufficient for good nutritional outcomes. Alderman and Garcia (1994) in their study in Pakistan found no significant association between food security, as measured by estimated per capita caloric availability, and stunting or wasting among preschool aged children. This was a departure from earlier work by Schnepf (1990) who found that in Rwanda per capita caloric availability was significantly associated with child stunting, wasting, and underweight.

There have also been studies examining the association between food security, as measured by variants of dietary diversity score, and child nutritional outcomes (see Ruel 2002 for a review). The majority of these studies found a positive association between dietary diversity and child nutritional outcomes. However, many of these studies lacked appropriate controls for socioeconomic and environmental factors, which could have confounded the association between dietary diversity and nutritional outcomes. Arimond and Ruel (2006) using the Demographic and Health Survey (DHS) data from 11 developing countries, show that dietary diversity has a strong bivariate association with heightfor-age z-score in nine of the eleven countries. In a multivariate analysis, controlling for biological and household socioeconomic factors, they find significant association between dietary diversity and heightfor-age Z-score in six of the eleven countries.

The same dietary diversity scores have also been validated with respect to other more conventional measures of food security. Hoddinott and Yohannes (2002) examine the correlations between dietary diversity and other measures of food security. Particularly, they find strong associations between dietary diversity and per capita expenditures on food, as well as between dietary diversity and number of calories consumed from non-staples. In most cases, the also find positive associations between dietary diversity and caloric availability from staples. More recently, Wiesmann et al (2009) also uses calorie consumption per capita as a benchmark to validate the FCS as an assessment tool. They find the associations between the FCS and calorie consumption to be both positive and statistically significant.

Even though the general flavor of analysis is similar, most of the existing research is generally in the spirit of validating particular measures of food security where the validation is done either in terms of correlation with other, existing measures of food security or in some cases with the nutritional outcomes. To the best of our knowledge, there hasn’t been any prior study that takes a variety of food security measures and attempts to systematically rank them by the strength of their correlations with nutritional 12
# **1. Introduction**

The interest rate channel of monetary policy is one important transmission channel through which monetary policy affects the real economy. The strength of the pass-through from the monetary policy rate to bank lending rates reflects the effectiveness of monetary policy and how the policy can help manage economic activity and stabilize prices. Central bankers are thus greatly interested in understanding the extent to which changes in the monetary policy rate transmit to lending rates and the factors that affect this transmission.

The interest rate pass-through is a keenly studied economic phenomenon. Because of imperfect information, competition, and other frictions, the pass-through from the monetary policy rate to various lending rates is, in practice, typically incomplete. The broader economic environment and various macro-financial factors may play an important role in different country contexts. Several recent studies summarize the empirical research on the interest rate pass-through. For instance, Sorensen and Werner (2006) survey 22 papers, covering aspects such as the type of data, econometric approach, reference rate, and time period. Andries and Billon (2016) review the empirical literature by comparing methodological approaches across studies and their implications for the estimated pass-through. Several papers also provide insights into the pass-through process from specific perspectives. For instance, Marotta (2009) reviews the literature on short-term business lending rates. Aziakpono and Wilson (2013) focus on the possible asymmetry in the interest rate pass-through, and Grigoli and Mota (2017) survey the pass-through studies for the Dominican Republic. Overall, these literature reviews confirm the heterogeneity in the interest rate pass-through across country contexts and lending rates. They suggest that the transmission of changes in the monetary policy rate to corporate lending rates may be the strongest, while the one to consumer lending rates may be the weakest.<sup>1</sup> Our paper contributes to the literature by conducting, to our knowledge, the first quantitative survey (a meta-analysis) of the empirical literature on the interest rate pass-through. In our meta-analysis, we

> 1 The literature also studies the transmission of changes in the monetary policy rate to deposit rates, which is documented to be weak on average. To ensure greater homogeneity of our meta-analysis, we focus our survey only on the transmission to lending rates.

2