rafmacalaba/datause-encoder-data huggingface dataset:
Table A1.1: Comparison of Treated and Untreated Firms

||Never<br>Treated|Treated|p-value|
|---|---|---|---|
|Proportion female<br>|0.51<br>|0.54<br>|0.710<br>|
|November2004profits|§029|6748|0.056|
|<br>Log November2004 profits<br>|8.15<br>|8.33<br>|0.224<br>|
|March2005 profits<br>|3260<br>|3454<br>|0.754<br>|
|Log March2005 profits|7.64|7.70|0.660|
|Education years|9.89|10.03|0.694|
|Age ofowner|41.05|42.59|0.318|
|Asset Index|-0.45|0.30|0.621|
|Previously had a loan|0.31|0.19|0.041|
|Proportion In manufacturing<br>|0.55<br>|0.48<br>|0.294<br>|
|Proportionmarried|0.84|0.85|0.958|
|<br>Log distance to coast<br>|5.60<br>|§.53<br>|0.317<br>|
|Proportion offilmsaged<=3years|0.22|0.21|0.842|
|Samplesize|83|117||

Table A1.2: Comparison of Manufacturing and Retail Firms

|Propartion|Manufacturing|Retail|p-value|
|---|---|---|---|
|<sup>female</sup><br>|0.59<br>|0.46<br>|0.054<br>|
|November2004profits|5065|6897|0.031|
|<br>Log November2004 profits<br>|8.03<br>|8.48<br>|0.001<br>|
|March2005profits|2733|3958|0.035|
|<br>Education years|10.11|9.67|0.211|
|Asset index|-0.45|0.48|0.908|
|Hasa majorsupplier|0.69|0.57|0.066|
|Hasa majorcustomer|0.42|0.21|0.000|
|Dailynumberofcustomers (March 05)|7.88|26.74|0.000|
|Percentofcustomers in same GN|72.52|75.78|0.484|
|Percentofinputs outside DS|22.15|21.01|0.824|
|Samplesize|107|102||

<!-- Start of picture text --> Daily number of customers (March 05) 7.88 26.74 0.000<br>Percent of customers in same GN 72.52 75.78 0.484<br>Percent of inputs outside DS 22.15 21.01 0.824<br>Sample size 107 102<br><!-- End of picture text -->
the characteristics of the household members which allows us to understand consumption and production patterns, and the impacts of any changes, on different socioeconomic and demographic groups.

To obtain estimates of the impacts of price changes on global poverty, we extrapolate from our sample of 31 countries to the world. This is done using the poverty results for countries for which we have household data as sample observations representing the income and regional groups from which they are drawn. Thus in addition to weighting countries’ results by their populations, we also weight each country’s result by the population weight which is necessary to make the countries used represent the region from which they are drawn. In the case of South Asia, where our sample covered more than 99 percent of the population living in the region, no regional weighting was necessary; however, in the case of MENA we had to raise the weight of the only country included in our sample, Yemen, to represent the whole region.

# **Simulations**

In our first set of simulations, we measure the impacts on poverty of uniform changes in all food prices for 10, 50 and 100 percent. In these highly stylized simulations, we focus on identifying the relationship between the severity of the general food price increase and global poverty in the short, medium and long run in a situation where the prices of all food commodities increase to the same extent.

For our short-run scenarios we use the first and second-order impacts on the expenditure side and only the first-order terms in the equations determining income changes. This assumes that consumption adjusts fully to the price changes, while production volumes do not change in the short run. In the medium- and long-run scenarios wages and agricultural output adjust in response to the changes in food prices; however, in the long-run the ability of the supply side of the economy adjust and of households to adjust their output is greater since both capital and labor are assumed to be mobile between sectors. We add one additional simulation which captures both the short run impacts and the medium run impacts on wages to allow us to assess the relative importance of wage rate and output adjustment impacts in explaining the differences between short and medium run results.

In order to demonstrate the difference between the implications of general food price increases and increases in prices of individual commodities, we include another set of 14
# **5.2 Empirical Studies of the Impact of Regulatory Agencies on Electricity Outcomes in DTEs**

To date, there have been very few studies applying econometric methods to a multicountry data set, including DTEs. We have found only two such studies that focus on this topic:

- (i) Bergara, Henisz and Spiller (1998); and

- (ii) Zhang, Kirkpatrick and Parker (2002).

There are some other studies which cover this topic among others. The most relevant is Pargal (2003). This explores the determinants of private investment in electricity in 9 Latin American countries along with private investment in various other infrastructure industries (roads, railways, water and telecoms).

# **5.2.1 Bergara, Henisz and Spiller (1998)**

The stated intention of this paper (henceforth BHS) is to explain the role of different countries institutional endowments on their electricity utilities’ investment decisions<sup>41</sup> . As the title of the paper suggests, the focus is on _political_ institutions and risk, including the legal system. However, the dependent variable in the BHS regressions is _total generating capacity (MW) per thousand population_ for some year (unstated) presumably in the mid-late 1990s rather than investment.

The independent variables include a few simple economic control factors – per capita GDP, industrial output as a share of GDP, urbanisation and the share of hydrogeneration.

Following Levy and Spiller, the BHS emphasis is on _political and legal institutions_ rather than regulatory agencies. Indeed, the latter are not included in the data set. The major problem for the analysis was that the individual political and legal variables were highly correlated with one another so that it was necessary to include a set of political variables as an index variable. Hence, BHS constructed two weighted average composite variables which were used in a regression on a sample of 86-87 countries. However, for a sub-sample of 38 countries, BHS had data on judicial tenure. They claim that this had the same explanatory power and magnitude as the other variables.

The overall BHS result was that increasing the degree of political commitment (as measured by their indices) of a middling score DTE by one standard deviation would be expected to raise the level of generating capacity by 1.2 MW per 1000 population – a 49% elasticity at the mean.<sup>42</sup>

> 41 Bergara, Henisz and Spiller op cit, p 20

> 42 See BHS op cit, pp 23-25.

33
<!-- Start of picture text --> = = 1 | iF | : | .I<br>= | T Po<br>art -et PELL PLL PPC Beemer sm| | | el<br>a a a a a: i<br>o o a az<br><!-- End of picture text -->

<!-- Start of picture text --> Privatizations as % of GDP - Per Year/Country<br>16<br>14<br>12<br>1<br>o8<br>06<br>o4 x 7<br>02<br>Le . 4<br>1 Te me<br>Low income LMI(exIND) UMI(exCHN) High income CHN IND<br>i Pre_2000 Ml Pre_GFC [ll Post_GFC<br><!-- End of picture text --> <!-- Start of picture text --> Privatizations as % of Gov't Revenue - Per Year/Country<br>6<br>5<br>4<br>3<br>1 - x i |<br>° :T E an<br>Low income LMI(exIND) UMI(exCHN) High income CHN IND<br>i Pre_2000 Ml Pre_GFC [lll Post_GFC<br><!-- End of picture text -->
<!-- Start of picture text --> REF IET ICC<br>0<br>-0.1<br>-0.2<br>AN1<br>NA1<br>-0.3<br>Global<br>-0.4<br>-0.5<br>-0.6<br>Change in EV (%)<br><!-- End of picture text -->

**Figure 2.** Changes in Hicksian equivalent variation of income (EV) in Annex I (AN1) and non-Annex I (NA1) countries, as well as globally, for the reference ( _REF_ ) scenario and for the integrated emissions-trading ( _IET_ ) and incentive-compatible ( _ICC_ ) representations of the CDM.

<!-- Start of picture text --> 20<br>15<br>10<br>5<br>IET<br>0 ICC<br>-5<br>-10<br>-15<br>P(ele, AN1) P(ele, NA1) Y(ele, AN1) Y(ele, NA1)<br>Changes in prices and output (%)<br><!-- End of picture text -->

**Figure 3.** Changes in electricity output (Y(ele)) and electricity prices (P(ele)) in Annex I (AN1) and non-Annex I (NA1) countries for the integrated emissions-trading ( _IET_ ) and incentive-compatible ( _ICC_ ) representations of the CDM relative to the _REF_ scenario.

27
power plants. The upper three figures are for all countries other than the top four countries with more than 200 power plants Figure 9: Global analysis at the country level: #plants and income <!-- Start of picture text --> Drop outliers (4 countries with over 200 power plants):<br><!-- End of picture text --> <!-- Start of picture text --> 6 8 10 12 20 25 30 35 .3 .4 .5 .6 .7<br>ln(GDP per capita) ln(GDP) ln(HDI)<br>200 200 200<br>150 150 150<br>100 100 100<br>#Power plants #Power plants #Power plants<br>50 50 50<br>0 0 0<br><!-- End of picture text --> When considering the relationship between national average SO2 concentrations and incomes, we find similar patterns that are consistent with the number of coal plants (Figure 10); i.e. countries with higher incomes tend to face more pollution from coal power plants. The correlation is positive and concave, suggesting that the increase in SO2 levels is slower at higher levels of GDP per capita. This finding is consistent with the notion that countries at higher income levels may move away from polluting energy sources, such as coal, and transition to cleaner, more efficient alternatives. National GDP also shows a positive but convex relationship with SO2, suggesting that higher GDP levels are associated with increasingly higher SO2 pollution. Our interpretation is that GDP measures economic output, while GDP per capita measures wealth or income. Economic outputs are mechanically always correlated with GDP, but income may show an increasing then decreasing pattern if rich people move to clean areas. We see similar estimates in tabular results in Table 3. GDP per capita is positively correlated with SO2, but the quadratic fit shows an increasing and then decreasing pattern.

Besides these country-level results, we also assess the relationship between income levels and pollution exposure by conducting a pixel-level analysis. Gridded GDP, GDP per capita, and SO2 data enable us to consider a sample of over 11 million unique locations (i.e. pixels). This enables us to capture granular sub-national variations, thus distinguishing between areas with different pollution and income levels with high precision of estimation. The results, summarized in Table 4, are consistent with the findings of the country-level analysis. Across the world, higher-income areas tend to be exposed to higher SO2 pollution from coal-fired power plants. Every additional 10% increase in GDP per capita is associated with 1.64% higher SO2 pollution.

We use additional flexible fittings to explore the potential nonlinear relationship between air 14
**Figure 1.1: Per capita income trends in a multi-polar world - emergence** **<u>of Brazil, China and India</u>**

<!-- Start of picture text --> $35,000<br>$30,000<br>$25,000<br>$20,000<br>$15,000<br>$10,000<br>$5,000<br>$0<br>1950 1965 1970 1975 1980 1985 1990 1995 2000 2005 2008<br>South Korea Brazil China<br>India United States Japan<br>Singapore Taiwan SSA-Madagascar<br><!-- End of picture text --> _Source: Angus Maddison Data Base (http://www.ggdc.net/maddison/)_ Among the set of developing countries, most raced to close the income gap with the developed countries under the leadership of their respective governments and support of international development agencies in the second half of the twentieth century but only a handful were successful in transforming their aspirations into reality. Among them were Japan and the East Asian Tigers (Hong Kong SAR, China; the Republic of Korea; Singapore; and Taiwan, China) (Figure 1.1).

The growth poles in the past were all high-income countries such as England, US, Japan and Germany. In the last quarter of the twentieth century this changed and for the first time, large developing countries such as China, India, and Brazil become growth poles. If the new growth poles continue to maintain their dynamic growth trends, their industries will upgrade to more capital intensive ones and leave a large space of labor-intensive industries for other low-income countries to enter. This paper tries to explore the implication of this new phenomenon for other developing countries, which still face the daunting challenges of achieving dynamic growth to reduce poverty.

# **_Multi-polarity and its potential dividends_**

Since the last quarter of the twentieth century, developing countries have played an increasingly important role in the global economy lending credence to the idea of multipolar growth being the new economic paradigm. In the 1980s and 1990s, among the top five contributors to global 4
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Campos, F., Goldstein, M., McGorman, L., Munoz Boudet, A. M., and Pimhidzai, O. (2015). Breaking the metal ceiling: female entrepreneurs who succeed in male-dominated sectors. _World Bank Policy Research Working Paper_ , (7503).

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Danon, A., Das, J., De Barros, A., and Filmer, D. (2023). Cognitive and socioemotional skills in low-income countries: Measurement and associations with schooling and earnings. _Journal of Development Economics_ , page 103132.

Das, S., Koroknay-Palicz, T., Marsh, V., McDaniel, D., and Rouanet, L. (2024). Socioemotional skills in africa: Development and validation of 14 measures in sub-saharan africa with implications for economic outcomes. _mimeo_ .

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24
# **5.1 Non-farm income as an exogenous transfer**

Under the hypothesis that non-farm income is an exogenous transfer, the Gini index can be decomposed into three parts: the share of each income component, the Gini index of each income component, and the Gini correlation of each income component. The first column of table 2 shows that the farm income is the largest component of the total income (58.6%), following by the non-farm income (31.5%). The second column shows that the Gini index of the distribution of the observed total income, including the contribution of non-farm activity, is 0.508; while that of the distribution of the farm income is 0.579. It suggests that, in the absence of non-farm activity, household income would have been 14.0% more unequal. In other words, the presence of nonfarm income reduces rural income inequality.

**<u>Table 2 Decomposition of Gini Index</u>**

||Percentage of<br>total income<br>_k_<br>_S_|Gini Index<br>_k_<br>_G_|Gini Correlation<br>_k_<br>_R_|Contribution to<br>inequality<br>0<br>_G_<br>_R_<br>_G_<br>_S_<br>_k_<br>_k_<br>_k_|
|---|---|---|---|---|
|Total income|100.0|0.508|1.00|100.0|
|Farm income|58.6|0.579|0.78|52.3|
|Agriculture|31.5|0.635|0.62|24.6|
|Livestock|16.0|0.729|0.45|10.3|
|Forestry and fishing|11.1|0.986|0.81|17.4|
|Non-farm income|31.9|0.828|0.79|40.8|
|Self-employment|27.1|0.927|0.82|40.7|
|Wage-paying|4.9|0.767|0.03|0.2|
|Other income|9.5|0.781|0.47|6.9|
|Private transfer|3.1|0.895|0.16|0.9|
|Others|6.4|0.849|0.56|6.0|

The LSMS data also show that the distribution of farm income is more equal than that of non-farm income. 94.3% of the households participate in farm activity, while only 68.2% participate in non-farm activity. 49% of the households have income from wage-paying activity, and 39% of households have income from self-employment activity. The distribution of the self13
Schools are compared across five key types of characteristics: scope (levels taught and number of shifts); infrastructure (building materials, water, latrines, etc.); physical materials (desks, chairs, blackboards, etc.); staffing; and students (number, gender, and repetition rates). As shown in Table 2, treatment schools were founded earlier than control schools on average, and have slightly more students in grades 5-6 (the final two grades of primary school). Along all other dimensions, treatment and control schools observed in both waves of the school census (and therefore having survived from 2002-3 through 2011-12) were observationally equivalent in 2002-3.

# **<u>Table 2: Comparison of treatment and control groups</u>**

||<br>Mean values obse<br>|rved in 2003<br>|<br>|
|---|---|---|---|
||Treatment|Control|P-value|
|School founding year|<br>1981|<br>1986|<br>0.004**|
|Scope||||
|Number of school levels taught|2.1|2.1|0.698|
|<br>Number of shifts|1.0|1.1|0.177|
|Infrastructureindex|-0.04|-0.09|0.147|
|Materialsindex|-0.03|-0.06|0.445|
|Staffing||||
|<br>Number of staff|6.35|5.93|0.215|
|Share of male teachers|0.69|0.73|0.154|
|Student-teacher ratio|35|37|0.422|
|Students||||
|Total number of students|198|186|0.382|
|Grades 1-4: total number of students|152|148|0.713|
|Grades 1-4: number of female students|71|73|0.711|
|Grades 1-4: number of male students|81|75|0.351|
|Grades 5-6: total number of students|47|38|0.083*|
|Grades 5-6: number of female students|22|19|0.156|
|Grades 5-6: number of male students|24|19|0.059*|
|Percent repeaters grades 1-6|0.15|0.14|0.761|
|_Number of observations_|_354_|||

Notes: Following Kling, Leibman, and Katz 2007, the infrastructure and materials indices are the equally weighted average z-scores of each component, where more beneficial outcomes have higher scores. The infrastructure index includes measures of a sports field, library, director’s office, recreation area, kitchen, latrines, water, electricity, roof, wall, and floor materials. The materials index includes measures of desks, chairs, and blackboards.

Source: World Bank staff estimates using Haiti national school census 2002-2003 9
# **ANNEX 5: CONCERNS REGARDING DATA**

# **Chilean survey**

One of the main problems in empirically modeling and analyzing the data from Chile is the nature of the data, which is mainly perceptional. This annex discusses concerns about the data focusing on possible pitfalls in the analysis and the conclusions. An additional concern is the possible inconsistency of the data. Finally there is a problem of selfselection in the models, since the empirical models use data from all the manufacturing firms when modeling the determinants of innovative outcome.

**The data is mainly perceptional.** There are concerns about perceptional data as they are reflections of the respondent’s subjective views. One concern regards innovation sales, particularly in innovation intensive sub-sectors of the manufacturing industry, where the percentages of sales allocated to innovative products or processes may not be consistent throughout the sample.

**Problems of comparing responses across the sample.** The importance of introducing a new product or process may also be different throughout the data. Different firms may attribute different importance to a new product or a new process if they are new only to the firm or new to the market. Therefore it is difficult to compare and use the information across the data.

**Inconsistency in the data** is also a concern. Before going deeper into a discussion about inconsistency it is important to state that the inconsistency problem is based on nonweighted data. The following states some examples of possible inconsistency in the data sample:

1. Product innovating<sup>22</sup> firms do not have any innovation expenses<sup>23</sup> 2. Firms with research and development departments do not have any research and development expenses

3. Several firms that reported having 30 percent or more of their sales come from innovation sales did not have any innovation expenses.

The data and the results/analysis from the data could improve significantly if these errors or mistakes were taken into consideration.

1. **Product innovating firms that do not have any innovation expenses.** There may be a reasonable explanation for this problem. The survey asks about product innovation within the last three years. In addition the innovation expenses, including the R&D expenditure, only concerns the year 2000 (and also a

> 22 Firms defined as product innovative have responded with the answer 1 – 4 to the three questions about product innovation in the questionnaire. The questions concerns: 1) improved technique in the products, 2) products new to firm, but known to the market and 3) products new to the firm and to the market.

> 23 Innovation expenses includes R&D expenses

35
%i i i i from country _e_ . Applying this percentage to corresponding FBS item’s carcass weight will yield ff,cc,ee<sup>indicates the percentage of a given FBS item</sup><sup>_f_consumed in Bangladesh imported</sup> all COOs from which this item was imported and its respective imported quantities.

For a given FBS item, the percentage of domestic supply that is domestically produced was used as a proxy for the percentage of consumption that is domestically produced. In this way, food loss and waste were implicitly included.

where the _f_ is a specific FBS food item and _c_ represents country, Bangladesh in this case.

# _2.3.3 Carcass weight_

Regarding quantities to be applied with GHG footprints, converting actual consumption to carcass weight requires two additional steps. We firstly converted edible portion to their raw weights using cooking yield factors (Bogna 2002 and USDA 2014). Secondly the raw weights were converted into carcass weights using extraction rates in Bangladesh (FAO 1997).

# **2.4 Data**

# _2.4.1 Data on household consumption_

Household-level consumption data are from HIES 2000 and HIES 2016,<sup>7</sup> which provide food items, quantities consumed, item values, and individuals’ basic information in a 14-day reference period. HIES 2000 includes 63 districts and 7,437 households with an average household size of 5.3 people. AME per household is 4.1 on average. HIES 2016 was designed to sample 46,076 households. But due to missing data of 94 households, the actual number of households used for calculations is 45,982 from 64 districts. The average household size was 4.1 people and AME per household was 3.3.

# _2.4.2 Data on nutrients_

In addition to macronutrients, we focused on six micronutrients, namely calcium, iron, zinc, Vit. A, Vit. B12, and folate, in terms of nutritional analysis. We based our decision of micronutrients on level of deficiency and availability of a micronutrient’s unit amount as a component of Bangladeshi local foods. Previous studies including Nutrients Background Paper (Talukder et al. 2015) and National Micronutrients Status Survey (ICDDR’B et al. 2013) mostly focus on Vitamin

> 7 Bangladesh Bureau of Statistics (BBS). Household Income and Expenditure Survey 2000/2016. Ministry of Planning, Government of Bangladesh. Data requests can be made at dg@bbs.gov.bd.

7