rafmacalaba/datause-encoder-data huggingface dataset:
# Measuring other indicators of well-being

To track welfare in countries without household consumption surveys, one can resort to using indirect measures, either non-monetary indicators or non-traditional surveys.

## _Multidimensional poverty index (MPI)_

Many countries have developed “multidimensional poverty indexes” (MPI) to complement existing monetary poverty statistics and provide a more comprehensive perspective on poverty. Expenditure measures may not accurately assess the cost of some goods and services not obtained through markets, thus prices do not accurately reflect their value. Examples of such goods and services include a clean environment, a secure community, public education and health. Measuring multidimensional poverty can be viable for tracking well-being were consumption data are missing.

## <u>Method</u>

The method for calculating MPI, briefly described below, is based on Alkire and Foster (2011). The Commission on Global Poverty, led by Sir Tony Atkinson, recommended the method to measure global non-monetary poverty.

The Alkire and Foster method (AF) consists of two steps. The first is to create a deprivation profile by comparing individuals or households to the respective deprivation cut-off point for selected indicators. The second step is to apply weights (which sum up to “1” or to 100 percent) to each of the indicators, and to sum them for each person to establish a deprivation score of weighted percentage of deprivations. Individuals are then identified as multidimensionally poor if the weighted sum of their deprivations is higher or equal to the poverty cut-off point (United Nations Development Program, 2019).

## <u>Data requirements and pitfalls</u>

Constructing MPIs is data-intensive and requires information on each deprivation for all households. Therefore, information should be based on micro-level data and should come from one source. This is challenging because it is not easy to find microdata with information on all dimensions of non-monetary poverty. Multiple Indicator Cluster Surveys (MICS) and Demographic and Health Surveys (DHS) collect comprehensive data on the health status of women and children but have limited employment data and no consumption data (UNDP, 2019).<sup>14</sup> The latest regional report on Arab multidimensional poverty (UN, 2017) covered seven countries and used, in most cases MICS, DHS, and Arab Family Health Project data. It did not construct MPI for Djibouti, the Syrian Arab Republic, Libya, and Lebanon because of a lack of recent data. The West Bank and Gaza was excluded due to unique problems related to blockade and movement restrictions. Table 4 presents

> 14 Many countries base MPIs on household budget surveys, which allows measuring both monetary and nonmonetary poverty. However, as noted in World Bank (2018), many surveys measure access to services and rarely the quality of services. Adding questions to measure quality of services may increase survey burden and jeopardize ability to monitor monetary poverty; so, there are trade-offs between the decision to collect monetary and/or nonmonetary information.

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the last half century, economists have responded enthusiastically to his postulated Kuznets Curve, searching for better data, better tests, and better models.

As we have said, Kuznets based his hypothetical Curve on very little evidence. The only country for which he had good data was the United States after 1913, on which he was the data pioneer himself. Beyond that, he judged earlier history from tax data taken from the United Kingdom since 1880 and Prussia since 1854 (1955, p. 4). For these three advanced countries, incomes had become more unequal between the late nineteenth century and the 1950s. He presented no data at all regarding earlier trends, yet bravely conjectured that “income inequality might have been widening from about 1780 to 1850 in England; from about 1840 to 1890, and particularly from 1870 on in the United States; and from the 1840’s to the 1890’s in Germany” (1955, p. 19). For poor, pre-industrial countries, he had only household surveys for India 1949-1950, Sri Lanka 1950, and Puerto Rico 1948 (1955, p. 20). These are all bad data judged by the standards Kuznets himself applied in the 1970s. They are also bad data judged by the modern World Bank standards since those three surveys from the mid-20th-century would now be given low grades on the Deininger-Squire scale assessing the quality of income distribution data (Deininger and Squire, 1996: pp. 567-71). Meanwhile, world inequalities have also changed. The convergence of incomes within industrial countries that so impressed Kuznets has been reversed, and the gaps have widened again.

We have reason, therefore, to ask anew whether income inequality was any greater in the distant past than it is today. This paper offers five conjectures about inequality patterns during and since ancient pre-industrial times: 3
Table 4 presents the estimates of equation (2). We include firm fixed effects only in column (1) and add year fixed effects, region-year fixed effects, and industry-region-year fixed effects in columns (2) through (4), respectively. Across all columns, we find negative coefficients with statistical significance at the 10% level. In column (4), the preferred specification, the coefficient estimate suggests that employment by the MNC group is 3.0% lower than by the non-MNC group.<sup>24</sup> In Appendix Table A.2, we repeat the analysis in Table 4 using the non-manufacturing group only and find that the treatment effect is statistically insignificant.

Table 4: MNCs and Employment in Manufacturing, 2013~2019

||Depend<br>(1)|ent Variable: <br>(2)|Log of Em<br>(3)|ployment<br>(4)|
|---|---|---|---|---|
|MNC_i ×_Post_t_|-0.0325*|-0.0312**|-0.0329***|-0.0295***|
||(0.0158)|(0.0114)|(0.0117)|(0.0101)|
|Observations|41,036|41,036|41,035|41,030|
|R-squared|0.9502|0.9502|0.9507|0.9541|
|Fixed Effects:|||||
|Firm|Yes|Yes|Yes|Yes|
|Year|No|Yes|No|No|
|Region-Year|No|No|Yes|No|
|Industry-Region-Year|No|No|No|Yes|

_Notes:_ MNC _i_ is an indicator variable that equals 1 if a firm _i_ has foreign affiliates in the years 2013~2016 and 0 otherwise. Post _t_ is an indicator variable that equals 1 if the time _t_ is after 2016 (i.e., 2017, 2018, and 2019) and 0 otherwise. Standard errors are clustered at the industry level. *** p<0.01, ** p<0.05, * p<0.1.

# **4.2.2 Dynamic Treatment Effects**

Although the difference-in-differences estimates can control for time-varying industryregion-specific shocks, one might still argue that they are not informative if those Korean firms with foreign affiliates would have experienced a slowdown in employment growth even in the absence of the labor market policy changes. This would be particularly the case if Korean firms with foreign affiliates had already been experiencing a relative decline in employment growth even before the labor market policy changes. To alleviate concern about a possible parallel trend, we check for differential employment trends between MNCs and non-MNCs in the three to the labor market years leading up policy

> 24We estimate equation (2) with a common linear time trend, excluding time fixed effects, and find that the coefficient of the common linear time trend is statistically indistinguishable from zero. We cautiously interpret the absence of the common linear trend as an indication that employment level for the non-MNCs group remained the same while the one for the MNCs group declined by 3.0%, corroborating the event study result in panel (a) of Figure 4.

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clustering of informal-sector businesses.<sup>23</sup> As noted, these comparisons are drawn relative to SRS (which readers will note is equivalent to ACS with ). All simulations set BA size to 150 by 150 meters<sup>24</sup> and use a neighborhood definition of eight adjacent BAs; they vary by changing CC= ∞ expansion thresholds ( ) and the initial selection number of BAs, . Later simulations build on these by also varying geographic scope, density, and by adding stratification. ii CC nn The simulations focus on the largest metropolitan area in Eswatini—Manzini and the periurban neighboring city of Matsapha. Together, the area covers approximately 120 square kilometers and has a daytime population of over 150,000. An overlay of BAs yields 5,616 BAs of 150 meters by 150 meters, as shown in Figure 3. These squares contain 4,471 establishments, the true value of the population total. The population appears clustered and rare, with only about 13% of squares containing any establishment at all. Simulations were run using Stata, and every simulation contains 10,000 initial draws of BAs. For comparison, two sets of simulations were run: one set for SRS and one for ACS. SRS simulations include samples ( 1) of 2%, 3%, 5%, 10%, 20% and 30% of the 5,616 BAs. ACS simulations include samples where the initial sample of nn BAs, before any expansions, cover 2%, 3%, 5%, 10%, and 20% of the 5,616 squares and examine the following possible values for . CC= {1, 2, 5, 10}

> 23 The simulation results presented here incorporate all 4,471 businesses, 1,901 formal and 2,570 informal businesses. Simulations run using only the informal businesses show similar clustering and yield similar results and efficiency gains.

> 24 The choice of a 150-meter-squared BA is as much practical as one driven by data: in an urban environment that can include large clusters of informality, interviewers must be able to enumerate a given BA in a reasonable amount of time. As described in later sections, the 150-meter-squared BA is an experience-based size that makes this fieldwork feasible.

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# **3.3.3. Feedback**

Feedback, defined as both formal and informal information from teachers (and peers, parents, etc) serves to provide students with a better understanding of their performance or learning (Hattie & Timperley, 2007; National Academies of Sciences, Engineering, and Medicine, 2018). Teacher feedback in general has been found to be one of the most powerful factors implicated in students’ academic learning and resultant achievement (Hattie & Yates, 2014). Teachers who give consistent, periodical, and process-oriented feedback are associated with self-regulated, high-achieving students (Good, Biddle, & Brophy, 1975; Good & Grouws, 1977; Hamre & Pianta, 2005; Nicol & Macfarlene-Dick, 2006; Taylor, Pearson, Peterson, & Rodriguez, 2003; Wharton-McDonald et al., 1998), decreased off-task behavior and disruptive classroom behaviors, and increased academic engagement (Johnson, Stoner, & Green, 1996; Lane, Wehby, Menzies, Doukas, Munton, & Gregg, 2003; Lo, Loe, & Cartledge, 2002; McNamara, Evans, & Hill, 1986; Sharpe, Brown, & Crider, 1995; Rosenberg, 1986).

Based on a review of the literature, teacher feedback, both positive or negative, can be one of the most powerful influences on student learning and achievement, though the type of feedback, its timing, and the way it is given can dictate its overall effectiveness (Hattie & Timperley, 2007). Empirical studies on teacher feedback underscore the importance of providing feedback that is instructive, timely, referenced to the actual task, and focused on what is correct and what to do next (Hattie & Timperley, 2007; Shute, 2008). Research has also addressed the use of metacognitive versus results feedback. Metacognitive feedback serves as cues about the content and structure of the problem and ways to solve it, whereas results feedback provides cues related to the final outcome of the problem. For example, Kramarski & Zeichner (2001) investigated the use of both types of feedback in a sixth grade mathematics class as a way to help students identify what to do to improve their performance. Students who received metacognitive feedback significantly outperformed students who received results feedback, in terms of mathematical achievement and the ability to provide mathematical explanations, such that they were more likely to provide robust explanations of their mathematical reasoning.

Based on this literature, _Teach_ included two behaviors to capture the extent to which the teacher provides feedback that can promote student comprehension: 23
# **Table A13: Identifying the $1.90 poor based on covariates of poverty**

|<br>|<br>|<br>_Tajikistan_<br>|<br>_2015_<br>|<br>|<br>||
|---|---|---|---|---|---|---|
|**Category**|**Per capita**<br>**poor only**|**Square root**<br>**poor only**|**Diff.**<br>**p-value**|**Per capita**<br>**poor**|**Square**<br>**root poor**|**Diff.**<br>**p-value**|
|**(1)**|**(2)**|**(3)**|**(4)**|**(5)**|**(6)**|**(7)**|
|Asset index|-0.011***|-0.009***|0.5362|-0.050***|-0.048***|0.5362|
|Access to electricity|-0.068|-0.009|0.2765|-0.255**|-0.196**|0.2765|
|Piped drinking water|-0.005|-0.006*|0.789|-0.021*|-0.022**|0.789|
|Improved sanitation|-0.007|-0.001|0.7581|-0.014|-0.009|0.7581|

<u>Source: Authors’ calculations from the Global Monitoring Database (GMD)</u>

# **Table A14: Identifying the $3.20 poor based on covariates of poverty**

|<br>|<br>|<br>_Tajikistan_<br>|<br>_2015_<br>|<br>|<br>||
|---|---|---|---|---|---|---|
|**Category**<br>**(1)**|**Per capita**<br>**poor only**<br>**(2)**|**Square root**<br>**poor only**<br>**(3)**|**Diff.**<br>**p-value**<br>**(4)**|**Per capita**<br>**poor**<br>**(5)**|**Square**<br>**root poor**<br>**(6)**|**Diff.**<br>**p-value**<br>**(7)**|
|Asset index|-0.024***|-0.027***|0.6774|-0.146***|-0.149***|0.6774|
|Access to electricity|0.031|-0.025|0.193|-0.301***|-0.357***|0.193|
|Piped drinking water|-0.014|-0.001|0.315|-0.074***|-0.061**|0.315|
|Improved sanitation|0.006|-0.011|0.668|-0.066|-0.082|0.668|

<u>Source: Authors’ calculations from the Global Monitoring Database (GMD)</u> 54
**<u>Table C.11. Descriptive statistics of estimation sample, West Bank and Gaza</u>**

|||**2007-20**|**09**||**2009-20**|**11**||**2011-2**|**017**|
|---|---|---|---|---|---|---|---|---|---|
||**2007**|**2009**|**difference**|**2009**|**2011**|**difference**|**2011**|**2017**|**difference**|
|L f i i|8.78|9.06|0.3***|9.06|9.13|0.1***|9.13|9.23|0.1***|
|og o per capta consumpton|(0.02)|(0.01)|(0.0)|(0.01)|(0.01)|(0.0)|(0.01)|(0.01)|(0.0)|
|Hd`|40.03|40.72|0.7**|39.80|41.65|1.9***|40.71|44.94|4.2***|
|eas age|(0.25)|(0.15)|(0.3)|(0.15)|(0.14)|(0.2)|(0.14)|(0.16)|(0.2)|
|Hd i fl|0.06|0.06|0.0|0.06|0.07|0.0*|0.07|0.08|0.0|
|ea s emae|(0.01)|(0.00)|(0.0)|(0.00)|(0.00)|(0.0)|(0.00)|(0.00)|(0.0)|
|Hd' hiht dti ll i i|0.55|0.50|-0.0***|0.50|0.51|0.0|0.51|0.51|-0.0|
|eas ges eucaon eve s prmary|(0.02)|(0.01)|(0.0)|(0.01)|(0.01)|(0.0)|(0.01)|(0.01)|(0.0)|
|Hd' hiht dti ll i d|0.18|0.17|-0.0|0.17|0.18|0.0|0.18|0.16|-0.0**|
|eas ges eucaon eve s seconary|(0.01)|(0.01)|(0.0)|(0.01)|(0.01)|(0.0)|(0.01)|(0.01)|(0.0)|
|Hd' hiht dti ll i tti|0.17|0.22|0.0***|0.21|0.22|0.0|0.22|0.23|0.0|
|eas ges eucaon eve s erary|(0.01)|(0.01)|(0.0)|(0.01)|(0.01)|(0.0)|(0.01)|(0.01)|(0.0)|
|Ub|0.54|0.70|0.2***|0.70|0.53|-0.2***|0.53|0.56|0.0***|
|ran area|(0.02)|(0.01)|(0.0)|(0.01)|(0.01)|(0.0)|(0.01)|(0.01)|(0.0)|
|Rf|0.18|0.12|-0.1***|0.12|0.21|0.1***|0.21|0.12|-0.1***|
|eugee area|(0.01)|(0.01)|(0.0)|(0.01)|(0.01)|(0.0)|(0.01)|(0.01)|(0.0)|

Note: ***, **, and * denote statistical significance at the 0.01, 0.05, and 0.10 levels. Standard errors are in parentheses. The data are unweighted. Household heads' ages are restricted to between 25 and 55 for the first survey round and adjusted accordingly for the second survey round. The reference groups are household with no primary education and living in rural areas.

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|2016<br>2,168|1,173|3,342|1,758|5,100|n.a.|n.a.<br>n.a.|
|---|---|---|---|---|---|---|

_Source: Nepal Electricity Authority (NEA). Annual Reports for the period between 2007 and 2017._ One of the limitations of shocking the load shedding in this analysis is that the model does not capture captive power generation to back up electricity supply during the load shedding. While not all industries can afford backup power generation, large industries invest in backup power supply. In such a case, large industries would not cut their production during the load shedding, instead they pay a higher price for electricity supplied through the backup system. However, lack of data does not allow us to model it. The problem lies in the data collection and reporting system adopted by the statistical organization that produces the input-output table, the primary source of data for CGE modeling. For example, when oil (diesel or fuel oil) is purchased by an industry, it can be used for multiple purposes. If it is diesel it can be used in heavy trucks, electricity generation and other processes. If it is fuel oil, it can be used in boilers, process heating and electricity generation. Industries do not keep records of how much oil is used for one purpose vs. others. Surveys for data collection from industries would be needed. Even if the surveys are carried out and the necessary data are collected, a major restructuring of the input-output (I-O) tables is further needed. I-O tables are periodically published by the National Statistical Offices, they report how much a commodity is used in an industry. It does not differentiate consumption of commodity (e.g., oil) in a production sector (say manufacturing) by type of end-use (freight transportation, power generation, heating, etc.). Therefore, representing captive power (e.g., backup electricity) in industry is not possible from the existing data reporting system. Similarly, the study has not captured the effect of investments households have made in their backup systems, such as storage devices (e.g., inverters, storage device powered lighting devices).

The implication of this limitation is that estimated impacts presented in the current analysis are higher than that would be when impacts of backup power supply systems in both production sectors and households are accounted for.

# **5. Results from Model Simulations**

# **5.1 Impacts on key macroeconomic variables**

Figure 2 presents the impacts of load shedding on key macroeconomic variables. These are the average annual deviation as compared to a hypothetical situation where load shedding would 9
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sample size is very large (5% of the US population) which is useful since foreign born are a small minority among certain racial groups, especially Blacks. Similarly, native born Asians only represent a small percentage of the population, as reported in panel A of table 1. More conventional data sets would not have contained enough observations under those categories for a meaningful analysis.

The primary categories used in the analysis are a simple set of eight interactions between four racial groups (Whites, Blacks, Asians and Hispanics) and two immigrant statuses (native or foreign born). Panel A of table 1 reports how the sample is distributed across those eight categories, for males and females respectively.

I include in the analysis individuals aged 25-65 which I take to be the main age range relevant for labor force participation. The choice of that age range explains why the percentages by racial group in the population might be different than those usually perceived. For example, the fraction of Hispanics in the age range 25-65 is lower than their fraction in the entire population, since Hispanics are a relatively young group.

The race categories in the IPUMS data are as follows: White, Black, American Indian or Alaska Native, Chinese, Japanese, Other Asian or Pacific Islander, Other Race, Two Major Races and Three or More Major Races. I excluded from the analysis individuals who fell under the following categories: American Indian or Alaska Native, Other Race, Two Major Races and Three or More Major Races. I grouped under Asians those who described themselves as Chinese, Japanese or Other Asian or Pacific Islander. In addition to the race variable, the IPUMS data contain a distinct “Hispanic Origin” variable, containing the following categories: Not Hispanic, Mexican, Puerto Rican, Cuban, and Other. I grouped individuals defined as Mexican, Puerto Rican, Cuban or Other (Hispanic) under “Hispanic”. I considered individuals who reported White under the race variable and Hispanic under the “Hispanic Origin” variable as Hispanics. By analogy to the exclusion of individuals declaring two or more races, I also excluded individuals who fell both under “Hispanic” and “Black” or “Asian”. This constituted a very small proportion of the sample.

4
The most powerful storms are predicted to destroy everything in their path. We assume that these storms destroy all the capital in the five coastal counties nearest the first point of contact. Table 4 presents the fraction of complete destruction storms in each simulation assuming a future baseline and the minimum pressure damage model. These complete destruction storms are present in all the simulations, including those from the present climate. However, there are consistently more of these storms in the future climate than the current climate. The climate models predict that these maximum damage storms currently range from 0.09% to 1.14% of all storms. Given the historic frequency of US storms of 2.3/yr, this implies maximum destructive storms would come to the US every 38 to 480 years given the current climate. With the future climate, the return period for the US would fall to between 18 years to 89 years. Of course, the return period of a maximum destruction storm for any specific location is much higher than the US value. It will depend on how frequently such a storm hits that specific location.

Table 4: Percent of Complete Destruction Storms

|Climate|Current|Future|
|---|---|---|
|Model|Climate|Climate|
|CNRM|0.23%|0.49%|
|ECHAM|0.09%|0.89%|
|GFDL|0.18%|0.71%|
|MIROC|1.14%|2.44%|

Note: Uses future baseline and minimum pressure damage function.

The use of four climate models provides a good sense of the role that climate uncertainty plays in this analysis. One can compare the results with each climate model to see what role that source of uncertainty contributes. The results are also sensitive to assumptions about economic growth and the damage function. As the economy grows faster or population increase more rapidly, there will be more in harm’s way. The minimum pressure model suggests that damages will increase less than proportionally 30