rafmacalaba/datause-encoder-data huggingface dataset:
**Figure 2. Poverty Reduction in South Asia 1970s-2000s**

<!-- Start of picture text --> 80<br>70<br>60<br>50<br>Headcount Index<br>40<br>(%) 1970s<br>30 2000s<br>20<br>10<br>0<br>Bangladesh India Nepal Pakistan Sri Lanka<br>Countries<br><!-- End of picture text --> Source: World Bank Regional Database.

Notes: 1) Poverty estimates use national poverty lines. The respective dates are: Bangladesh (1975 and 2005); India (1974 and 2005); Nepal (1977 and 2004); Pakistan (1970 and 2005); and Sri Lanka (1976 and 2005).

income growth for the poor, higher employment elasticity of growth, and strengthened public service delivery including better social protection policies all need to be core elements of a strategy to lower income inequality.

# **II. Growth Acceleration, Lagging Regions**<sup>2</sup> **and Inequality: A Framework**

The experience of East Asia shows that growth that is supported by factor accumulation as well as productivity improvements can lead to higher growth.<sup>3</sup> South Asia’s experience is similarly positive (Ahmed 2006). Additionally, it has two key assets, demography and geography, which have not yet been fully utilized. It has a young labor force. More workers will join the labor force over the coming decades. Though the small size of the manufacturing sector has prevented the region from converting the demographic dividend into a window of opportunity, the large and potentially competitive labor force could be the catalyst that could attract regional and global production centers to be located in South Asia, as firms move in response to wage differences, and globalization benefits low-income countries. South Asia’s geography also has the potential to

> 2 A lagging region is defined as a poor region that does not grow as fast as the others, and its per capita income is low compared to national average.

> 3  See Gill and H. Kharas, 2007

3
5- Estimate the expected mean of the natural log. per capita income (ln I�I ı ), the residuals i and using estimation of step 4. eê uu�6- Sum ooii i and and estimate the expected total variance following equation 7: 2 ooii i � 0 1 2 i 3 i 4 i i . eê uu� ooii ii ii �uu� + eê = ππ + ππ NN + ππ XX NN + ππ XX + ππ tt + θθ Identify the significant interactions parameters to keep them in the estimation of the final equation 7. 7- Predict the expected variance ( 2 i ) using estimation of step 6 including only significant interactions. σσ�uuooii+ee � ln P ı 8- Compute the probability of being vulnerable to poverty as i ~~(~~ ). 2 −lnIIPPI ~~�~~<sup>σσ�</sup> VV<sup>�</sup> = ΦΦ uuooii+eei 9- Classify the households as vulnerable and non-vulnerable using the threshold of 0.29.

10.4- Classify the households as vulnerable and non-vulnerable using the threshold of 0.29. 11- Classify the households as PI those vulnerable households from step 9 with ln I�I i 12- Classify the households as RI those vulnerable households from step 9 with ln I�I i < ln IIPP. ln I <mark>14- <13?></mark> �I i Classify the households that under steps 9 and 10.4 are vulnerable as CRI if their We interpret these households as vulnerable to poverty due to climate or natural > ln IIPP. disaster risk. These are the households that are not PI but can be pushed into poverty due to CR. > ln IIPP.

# **3 Data**

As we mentioned, we exploit five years of survey data per country to expand the variability of data, specifically in small countries such as the Dominican Republic, El Salvador, Honduras, and Panama. The surveys provide information at the individual and household levels. The data source is the SEDLAC database, which provides harmonized and standardized household survey data for LAC countries. Although most household surveys are representative at the state (province) level, some allow to identify lower levels, such as districts or localities where households reside. Thus, we also exploit information of climate hazards at the administrative unit level where each household is located. In our model, to construct the first group of variables (the dependent variable, IPCF, and household characteristics regressors, ), we need information at level 1. Hence, we use survey data at household level. The second group of variables is at level 2 and corresponds to community characteristics XX (regressors of aggregate household characteristics climate change and community natural hazards ). The aggregate data is prepared at FAO Global Administrative Unit Layers (GAUL) using ZZ, II, ADM 1, ADM 2, or ADM 3, depending on the imputation level. NN 10
**Table 8: Forest products residues and some woody energy crops costs for the biorefineries**

|Source|Feedstock|Estimat<br>$/ton|ed cost<br>$/lt ethanol|
|---|---|---|---|
|NREL (1998)|Hardwood primary mill residue|33.9|0.113|
|NREL (1998)|Softwood primary mill residue|34.6|0.115|
|NREL (1998)|Hardwood secondary mill residue|30.5|0.102|
|NREL (1998)|Softwood secondary mill residue|30.4|0.102|
|Junginger et al. (2005)<sup>a</sup>|Primary forest fuel (residues)|27|0.09|
|Frederick et al. (2008)|Yellow poplar|48.1|0.160|
|Frederick et al. (2008)|Loblolly pine|67.0-71.5|0.22-0.24|
|Manzone et al. (2009)<sup>b</sup>|Poplar|110-132|0.365-0.438|

Source: Elaborated by the authors.<sup>a</sup> Original reported in 2002 euros/Gj, and was converted using 21.1 Mj/lt of ethanol (LHV) a yield of 300lt/ton of forest residues, an exchange rate of 1.08 euros/dollar, and updated to 2008 dollars using the GDP deflator (multiplied by 1.175).<sup>b</sup> Under conditions in Italy; original in euros/ton, converted with an exchange rate of 0.68 euros/dollar and 300lt of ethanol per ton of biomass.

As with other feedstocks, the estimation of production costs of herbaceous energy crops is not standardized, and thus not surprisingly the literature reports widely divergent figures (see Table 9). Their costs of production change with yield and land rent charges, which can vary widely. This is because land rent charges vary spatially reflecting the expected profitability of the options available to producers. _Ceteris paribus_ , better soils will have higher agricultural returns and thus higher per hectare opportunity costs for feedstock production. On the other hand, higher yields tend to lower the opportunity cost of land by diluting these over more tons of feedstock. Some examples of the impacts of these assumptions can be obtained from Table 9. Epplin et al. (2007) used land rent costs in Tennessee of $60 per acre for long term leases, and yields of 5.5 tons/ acre. For production in Nebraska and South Dakota, Perrin et al. (2008) used land rents ranging from $26 to $90 per acre, contingent on the field location. For his base case, Duffy (2007) assumed land costs in Iowa of $80 per acre and yields of 4 tons/acre. The author also conducted a sensitivity analysis over a range of values for these parameters, as well as storage and transportation costs. Also for the case of Iowa, Babcock et al. (2007) obtain relatively high costs of production, using a different approach. These authors argue that in order for switchgrass to bid area away from corn and soybeans in the Corn Belt, the herbaceous crop should provide similar expected returns over variable costs of 20
<!-- Start of picture text --> 50.0 Wave 2 posterior regret in IVR experiment<br>37.5<br>25.0<br>12.5<br>0.0<br>0.000 0.001 0.002 0.003<br>Posterior regret associated with the treatment arm selected<br>Count<br><!-- End of picture text --> Figure 5: Distribution of the posterior expected regret from wave 1 data and 200 simulated samples for wave 2, based on _β_<sup>_E_</sup> , _κ_<sup>_E_</sup> and _η_<sup>_E_</sup> drawn from their posterior distributions after wave 1.

1. The posterior expected regret at _t_ = 1 would be the basis for decision making if no other wave was conducted, and T1B was the arm with the lowest expected regret at that point. Panels B and C show the results of the simulations of wave 2. Panel B shows the average and median posterior policy regret and probability optimal of the chosen arm. On average, the simulation predicts an improvement in expected regret from continued experimentation of 0.04%, and an increase in the probability optimal for the chosen arm from 74.14% to 77.86%. Using the median of the distribution, the improvement would be 0.07% and to a probability optimal of 83.01%. Note that the average posterior expected regret has a heavily skewed distribution. The actual value of 0.02% observed after the second wave in the IVR experiment is at the 38th percentile of that distribution, as seen in Figure 5.

Panel C shows the average realized policy regret and the percentage of times the best arm is identified, both after wave 1 (where the experimenter would have chosen arm T1B) and after wave 2. Comparing the numbers for wave 1 with Panel A shows that the distribution of the simulated draws replicates the theoretical posteriors, as expected. The numbers for wave 2 show realized gains that more than halve the predicted policy regret of wave 1 and increase the share of optimal decisions by 10%. In the actual IVR experiment, had we conducted these calculations between waves, we would have likely concluded that the low monetary cost of sending the IVR calls to the second half of the sample would have more than justified the gains in certainty about the optimal choice. The actual IVR experiment performed even better than these simulations predict. A better prior for our parameters, for example based on pilot data, is likely to generate more reliable answers to research design questions.

42
**Assumption 1.** _Define_

_for some b. We observe F_<sup>ˆ</sup> 1( _y_ ) _and F_<sup>ˆ</sup> 2( _y_ ) _, pointwise consistent estimators of F_ 1( _y_ ) _and F_ 2( _y_ ) _respectively._ The first assumption simply states that out of the available information (for example, household surveys or tax records) we can obtain consistent estimators of the conditional distribution of income; _F_<sup>ˆ</sup> 1 estimates the conditional distribution below an income threshold _b_ , while _F_<sup>ˆ</sup> 2 estimates the conditional distribution for incomes above _b_ .

**Assumption 2.** _F_<sup>ˆ</sup> ( _b_ ) _, a consistent estimator of F_ ( _b_ ) _, is observed._

The second assumption is necessary to combine the information in _F_<sup>ˆ</sup> 1 and _F_<sup>ˆ</sup> 2 correctly into an estimator of _F_ . This assumption does not require knowing the full distribution _F_ , only the value at point _b_ .

# **2.2 Results**

We provide two simple results. The first proposition shows that under assumptions 1 and 2, we can obtain a consistent estimate of the distribution _F_ . The second proposition shows that it is possible to express the Gini coefficient of the income distribution _F_ as a combination of Gini coefficients constructed using the conditional distributions _F_ 1 and _F_ 2.

**Proposition 1.** _Under Assumptions 1 and 2, the estimator F_<sup>ˆ</sup> ( _y_ ) _constructed as:_

_is a pointwise consistent estimator of F_ ( _y_ ) _._ 5
Table 1 provides the values of _OEW_ * at mean points for _X_ and for two reference utility levels (as defined by the middle two cut-off points from the Ordered Probits reported in Ravallion and Lokshin (2007). The proportion of the population reporting that they are below the lower of these two cut-off points (i.e., that they are either ‘very unsatisfied’ or ‘unsatisfied’) corresponds closely to prevailing estimates of the poverty rate for Malawi.

**<u>Table 1: Poverty lines for own-economic welfare in Malawi holding utility constant</u>**

|||**Neighbors** (_NE_<br> <br>|_W_)<br>||
|---|---|---|---|---|
||**1 (poorest)**|**2**<br>**3**|**4**|**5**|
|(a)At the bo<br>)|undary between|“unsatisfied”and“neither|unsatisfiednor|satisfied”|
|**1 (poorest)**<br>_FEW_|2.88|2.85<br>2.82|2.78|2.74|
|**2**<br>**s** (|2.82|2.79<br>2.75|2.70|2.64|
|**3**<br>**nd**|2.76|2.72<br>2.66|2.60|2.53|
|**4**<br>**rie**|2.68|2.62<br>2.56|2.47|2.36|
|**F**<br>**5**|2.58|2.50<br>2.41|2.29|2.13|
|(b)At the bo|undary between|“neitherunsatisfiednorsa|tisfied”and“sa|tisfied”|
|**1 (poorest)**|4.01|4.05<br>4.11|4.17|4.24|
|**2**<br>|4.06|4.12<br>4.18|4.26|4.35|
|**3**<br>_EW_|4.13|4.20<br>4.27|4.37|4.48|
|**4**<br>_F_|4.21|4.29<br>4.39|4.51|4.67|
|<br>**5**|4.31|4.42<br>4.55|4.72|4.93|

<u>Note: The table gives the value</u> _OEW_<sup>_*_</sup> needed to compensate for each ( _NEW_ , _FEW_ ) combination holding the expected value of utility constant. Controls set to mean points. <u>Source: Author’s calculations for this paper.</u>

* Focusing first on panel (a) in Table 1, one finds that for _u_ at the estimated cut-off point below which one is either ‘very unsatisfied’ or ‘unsatisfied,’ the value of _OEW_ * tends to fall as _FEW_ rises (at given _NEW_ ) and similarly _OEW_ * falls as _NEW_ rises (at given _FEW_ ).  This pattern reflects the positive utility gains from having better off friends and neighbors.  However, the pattern reverses when one shifts to the higher reference utility level (panel b), when the RD effect kicks in.  Then the poverty lines in the space of perceived economic welfare start to look * like relative poverty lines.  However, note that in all cases in Table 1, the gradients in _OEW_ are * small; to the nearest ladder rung, the poverty line is at _OEW_ = 3 for 80% of cases in panel (a) * and it is _OEW_ = 4 for 80% of cases in panel (b).

19
<!-- Start of picture text --> 02<br>O1<br>|<br>gSs d<br>-0.203<br>04<br>-0.5 -10a 5 0 |5 10<br>Periods to Treatment<br><!-- End of picture text -->

<!-- Start of picture text --> 02<br>Ol<br>oo | fg...<br>5s a<br>02<br>03 : : :<br>04<br>0.5<br>-10a|5 0 5 10<br>Periods to Treatment<br><!-- End of picture text -->
academic program over time and is not university-specific. There are 17 academic programs across the 37 public universities, as shown in Table A3.2. For instance, if there is an expansion of a particular academic program (e.g., engineering or medicine) in time _t_ , such expansion increases the availability of choices for applicants. Thus, an applicant who is determined to study a specific concentration (e.g., civil engineering, econometrics) will likely utilize the expansion and select their preferred concentration in newly established academic programs across universities. This factor changes the composition of applicants enrolled in specific academic programs over time and is controlled by _γut_ .

The inclusion of these fixed effects is important for the DD estimator to differentiate trends and account for common shocks to attain a credible estimate of the quota policy’s impact on the outcomes of interest. The identification strategy provides a causal effect as long as the central assumption of parallel trends between the treated and untreated units in the absence of the policy (Angrist & Pischke, 2009; Cunningham, 2021; Huntington-Klein, 2022; Morgan & Winship, 2014), conditional on fixed effects, is not violated. If this assumption holds, we expect that the outcome trends between the treated and comparison groups, conditional on fixed effects, would follow a similar pattern to that between the two groups in the pre-treatment period. To check for parallel pre-treatment trends, I ran the following event study model: In Equation 2, the baseline year is the year before to the policy adoption in concentration _isu_ , I allowed for dynamic leads and lags, and the remaining terms are described in Equation 1. I find plausible evidence from the event study analysis reported in Appendix B.4, that the trends between the treated and controls units, conditional on fixed effects, are not statistically different in the pre-treatment period. The policy was implemented without advance announcements before its enactment in 2016, limiting any anticipation effect.

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be outside the domain of individual responsibility, is in fact higher than the true upper bound. But if we recognize the fact that talent (or ability) is be a function of cultivated talent just as well as innate talent and that the ability to cultivate and develop talent or skills over a lifetime may, in turn, depend on birth circumstances, then the upper bound we report here will be closer to the what would be the true upper bound of the inequality due to circumstances along the lines argued by, for example, Kanbur and Wagstaff (2014).

**Figure 4. Total inequality and the bounds on inequality of opportunity in Russia, Germany and the United States**

<!-- Start of picture text --> 64% 64%<br>62%<br>34%<br>31%<br>24%<br>17%<br><!-- End of picture text --> Notes: Authors’ calculations for Russia. Numbers for Germany and the USA are from Niehaus and Piechel (2012).

Finally, we juxtapose our main results for Russia against corresponding numbers calculated for Germany and the United States by Niehues and Peichl (2012) to compare inequality of opportunity in wage earnings in the three countries. As can be seen in Figure 4, the overall inequality in labor earnings is lower in Russia than in both the US and Germany. However, once we start looking at the share of this inequality that is unfair in the sense that we have described in this paper, then the narrative reverses. The lower bounds are not strictly comparable here (as the circumstances used are different), however, if one looks at the first upper bound with the individual fixed effect, it is clear that the inequality of opportunity shares are markedly higher in Russia in comparison to both the US and Germany. To be precise, while 64% of the total wage earnings inequality in Russia is of the “unfair” kind, only 31% is unfair in the US and 48% in Germany. Another interesting observation that can be made in Figure 4 is the fact that the difference between the two upper bounds appears substantially smaller for Russia in comparison to the US and Germany. Recall that the difference is a measure of the extent to circumstances affect inequality indirectly through effort. While there are several factors relating to the comparability of the samples for the three countries, one possible and rather speculative reason for this difference could be that educational 12
_Background: currentpolicies on_ **_entry_** _into infrastructure_ _Section 2_ As far as we are aware, most of the countries which legally sanction free entry in their formal water and power sectors are developed nations. In most cases a licence is required and free entry is restricted to certain areas. For example:

- most Australian States permit free entry in electricity generation (although licences are needed to join power pools in most States), and are liberalising entry in electricity retailing;

- the UK allows entry in: gas (retail and network extensions); electricity (retail and new connections but not network extensions); and water distribution through 'inset appointments'. In all of these cases a licence is required, but the main requirement is that basic minimum technical standards are met.

- New Zealand allows entry in electricity, gas and telecommunications. Entrants are generally required to register with the Government, and comply with competitively neutral regulations such as information disclosure requirements.

Free entry was important in the development of infrastructure during industrialisation in the UK and USA. For example, London's water supply system was constructed by a number of private companies most of which had non-exclusive statutory authorisations from the Government, and competed with each other in a variety of ways.<sup>4</sup> Similarly in the US, early railroad investment often required legal authorisation, but these authorisations could be non-exclusive, and railroads competed with each other.<sup>5</sup> Examples of less developed countries that allow free entry in formal and large scale infrastructure provision include:

- Argentina, which allows competitive entry in power generation and in electricity and gas marketing (retail supply). The transmission and distribution networks are the subject of concessions, but must provide access to competing producers and retailers; and

- Chile allows free entry into the gas transmission pipeline business, which resulted in competition between developers - the so-called 'pipeline war' ;6

Many countries permit legal or de facto free entry into small scale and informal provision of infrastructure-related services. A UNDP-World Bank study highlights the reasons for this, and its importance;

- 4 Tynan and Cowen (1998).

- _5_ Chernow, Ron (1997) p.<sup>22</sup> -<sup>2 5</sup> .; and Wasserstein, Bruce (1998) _Big Deal: The Battle for control ofAmerica's Leading Corporations_ Warner Books Inc. p.29-35.

- 6 Warwick Smith, World Bank - personal communication.

_18_
to 22 percent in 2014, with a sharp increase in urban areas reaching nearly 40 percent in 2014. Female youth unemployment decreased by 2 percentage points during the same period but remained higher than male youth unemployment (Figure 8). The gender gap is also pronounced across many states with lower poverty incidence, a pattern similar to all working-age adults, as explained earlier.

## **Figure 8. Youth unemployment rate by** **<u>gender, quintile, locality, and year</u>**

<!-- Start of picture text --> 50%<br>40%<br>30%<br>20%<br>10%<br>0%<br>All youth Women Men B20% B40% T20% Rural Urban<br>By gender By quintile By locality<br>2009 2014<br><!-- End of picture text --> _Source:_ World Bank 2019b.

# **_Employment sectors and type of work_**

Women are overrepresented in non-paid jobs, while men constitute most of the employers, paid workers, and own account workers. Employment types are categorized as paid employee, employer, own account worker, unpaid family worker, and unpaid working for others. This categorization is important as it sheds light on why there may be differences in incomes and access to social benefits across different groups. For example, non-paid jobs correlate with low incomes and less likelihood of having health insurance coverage—benefits that are relatively common among paid employees. Among the non-paid workers, 64 percent are women. Only one-fifth (21 percent) of paid employees are women, and even less are employers (18 percent). Youth represent only 20 percent of the working population, yet constitute more than 40 percent of the unpaid workers, and only represent 18 percent of the paid employees, 13 percent of own account workers, and 10 percent of employers (Figure 9). Unpaid work within the family is the most common type of employment for women, particularly for younger women in rural areas.

14